{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "L4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Track A"
      ],
      "metadata": {
        "id": "PXbaFlrjpclB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MmwTHIyfoVfv",
        "outputId": "465d69ff-0cbd-474d-f22f-b3a646139305"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->timm) (1.3.0)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets>=8.0.4->open3d==0.17.0) (0.8.4)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets>=8.0.4->open3d==0.17.0) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=6.1.0->ipywidgets>=8.0.4->open3d==0.17.0) (0.2.13)\n",
            "Installing collected packages: dash-table, dash-html-components, dash-core-components, addict, widgetsnbextension, retrying, pyquaternion, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, jedi, configargparse, comm, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, meshio, ipywidgets, dash, nbformat, open3d, timm\n",
            "  Attempting uninstall: widgetsnbextension\n",
            "    Found existing installation: widgetsnbextension 3.6.7\n",
            "    Uninstalling widgetsnbextension-3.6.7:\n",
            "      Successfully uninstalled widgetsnbextension-3.6.7\n",
            "  Attempting uninstall: ipywidgets\n",
            "    Found existing installation: ipywidgets 7.7.1\n",
            "    Uninstalling ipywidgets-7.7.1:\n",
            "      Successfully uninstalled ipywidgets-7.7.1\n",
            "  Attempting uninstall: nbformat\n",
            "    Found existing installation: nbformat 5.10.4\n",
            "    Uninstalling nbformat-5.10.4:\n",
            "      Successfully uninstalled nbformat-5.10.4\n",
            "Successfully installed addict-2.4.0 comm-0.2.2 configargparse-1.7 dash-2.17.1 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 ipywidgets-8.1.3 jedi-0.19.1 meshio-5.3.5 nbformat-5.7.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 open3d-0.17.0 pyquaternion-0.9.9 retrying-1.3.4 timm-1.0.7 widgetsnbextension-4.0.11\n"
          ]
        }
      ],
      "source": [
        "!pip install timm meshio open3d==0.17.0 gdown"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pip install paddlepaddle-gpu==2.6.1.post120 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gTQQsxqSoobO",
        "outputId": "f42225f9-f925-4330-c5f1-ede19a07c9b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in links: https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html\n",
            "Collecting paddlepaddle-gpu==2.6.1.post120\n",
            "  Downloading https://paddle-wheel.bj.bcebos.com/2.6.1/linux/linux-gpu-cuda12.0-cudnn8.9-mkl-gcc12.2-avx/paddlepaddle_gpu-2.6.1.post120-cp310-cp310-linux_x86_64.whl (796.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m796.8/796.8 MB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting httpx (from paddlepaddle-gpu==2.6.1.post120)\n",
            "  Downloading httpx-0.27.0-py3-none-any.whl (75 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.6.1.post120) (1.25.2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.6.1.post120) (9.4.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.6.1.post120) (4.4.2)\n",
            "Collecting astor (from paddlepaddle-gpu==2.6.1.post120)\n",
            "  Downloading astor-0.8.1-py2.py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: opt-einsum==3.3.0 in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.6.1.post120) (3.3.0)\n",
            "Requirement already satisfied: protobuf>=3.20.2 in /usr/local/lib/python3.10/dist-packages (from paddlepaddle-gpu==2.6.1.post120) (3.20.3)\n",
            "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx->paddlepaddle-gpu==2.6.1.post120) (3.7.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx->paddlepaddle-gpu==2.6.1.post120) (2024.7.4)\n",
            "Collecting httpcore==1.* (from httpx->paddlepaddle-gpu==2.6.1.post120)\n",
            "  Downloading httpcore-1.0.5-py3-none-any.whl (77 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx->paddlepaddle-gpu==2.6.1.post120) (3.7)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx->paddlepaddle-gpu==2.6.1.post120) (1.3.1)\n",
            "Collecting h11<0.15,>=0.13 (from httpcore==1.*->httpx->paddlepaddle-gpu==2.6.1.post120)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx->paddlepaddle-gpu==2.6.1.post120) (1.2.2)\n",
            "Installing collected packages: h11, astor, httpcore, httpx, paddlepaddle-gpu\n",
            "Successfully installed astor-0.8.1 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 paddlepaddle-gpu-2.6.1.post120\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown --id '1vutMhL-GAuVqhyaRSOVBMy3JcJjlNGfv' --output data.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ozTCi8hNoo4z",
        "outputId": "da44c6c8-f099-4ef0-9fe3-7e0b0845383c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/__main__.py:132: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1vutMhL-GAuVqhyaRSOVBMy3JcJjlNGfv\n",
            "From (redirected): https://drive.google.com/uc?id=1vutMhL-GAuVqhyaRSOVBMy3JcJjlNGfv&confirm=t&uuid=953f99ea-004d-480b-92a2-cea73eb8afc3\n",
            "To: /content/data.zip\n",
            "100% 1.42G/1.42G [00:46<00:00, 30.7MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip data.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oMyVJWGSoq1Y",
        "outputId": "40573f97-1801-4bff-dcc5-9a39cfe6877a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  data.zip\n",
            "   creating: drive/MyDrive/Colab Notebooks/\n",
            "  inflating: drive/MyDrive/Colab Notebooks/Untitled  \n",
            "  inflating: drive/MyDrive/Colab Notebooks/Untitled0.ipynb  \n",
            "  inflating: drive/MyDrive/Colab Notebooks/submit_B.ipynb  \n",
            "  inflating: drive/MyDrive/Colab Notebooks/track_A  \n",
            "   creating: drive/MyDrive/configs/\n",
            "  inflating: drive/MyDrive/configs/SDF_GCN_FNO.yaml  \n",
            "  inflating: drive/MyDrive/configs/base.yaml  \n",
            "   creating: drive/MyDrive/data/\n",
            "   creating: drive/MyDrive/data/data_test_A/\n",
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            "   creating: drive/MyDrive/data/data_test_B/\n",
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            "  inflating: drive/MyDrive/model.pdparams  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import argparse\n",
        "import csv\n",
        "from timeit import default_timer\n",
        "import yaml\n",
        "from paddle.optimizer.lr import LRScheduler\n",
        "import re\n",
        "import meshio\n",
        "import warnings\n",
        "from pathlib import Path\n",
        "from typing import List\n",
        "from typing import Tuple\n",
        "from typing import Union\n",
        "import numpy as np\n",
        "import open3d as o3d\n",
        "# import open3d.cpu.pybind.t.geometry\n",
        "import paddle\n",
        "import paddle.nn as nn\n",
        "import paddle.nn.functional as F\n",
        "import os"
      ],
      "metadata": {
        "id": "3na-KQ1votNF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class DotDict(dict):\n",
        "    \"\"\"\n",
        "    dot.notation access to dictionary attributes\n",
        "    \"\"\"\n",
        "\n",
        "    __getattr__ = dict.get\n",
        "    __setattr__ = dict.__setitem__\n",
        "    __delattr__ = dict.__delitem__\n",
        "\n",
        "    def __init__(self, *args, **kwargs):\n",
        "        super().__init__(*args, **kwargs)\n",
        "        for arg in args:\n",
        "            if isinstance(arg, dict):\n",
        "                for k, v in arg.items():\n",
        "                    self[k] = v\n",
        "\n",
        "        if kwargs:\n",
        "            for k, v in kwargs.items():\n",
        "                self[k] = v\n",
        "\n",
        "    def __repr__(self):\n",
        "        return f\"{self.__class__.__name__}({super().__repr__()})\"\n",
        "\n",
        "\n",
        "def flatten_dict(d, parent_key=\"\", sep=\"_\", no_sep_keys=[\"base\"]):\n",
        "    items = []\n",
        "    for k, v in d.items():\n",
        "        # Do not expand parent key if it is \"base\"\n",
        "        if parent_key in no_sep_keys:\n",
        "            new_key = k\n",
        "        else:\n",
        "            new_key = parent_key + sep + k if parent_key else k\n",
        "        if isinstance(v, dict):\n",
        "            items.extend(flatten_dict(v, new_key, sep=sep).items())\n",
        "        else:\n",
        "            items.append((new_key, v))\n",
        "    return dict(items)\n",
        "\n",
        "class AverageMeter:\n",
        "    def __init__(self):\n",
        "        self.reset()\n",
        "\n",
        "    def reset(self):\n",
        "        self.val = 0\n",
        "        self.avg = 0\n",
        "        self.sum = 0\n",
        "        self.count = 0\n",
        "\n",
        "    def update(self, val, n=1):\n",
        "        self.val = val\n",
        "        self.sum += val * n\n",
        "        self.count += n\n",
        "        self.avg = self.sum / self.count\n",
        "\n",
        "# Average Meter with dictionary values\n",
        "class AverageMeterDict:\n",
        "    def __init__(self):\n",
        "        self.reset()\n",
        "\n",
        "    def reset(self):\n",
        "        self.val = {}\n",
        "        self.avg = {}\n",
        "        self.sum = {}\n",
        "        self.count = {}\n",
        "\n",
        "    def update(self, val, n=1):\n",
        "        for k, v in val.items():\n",
        "            if k not in self.val:\n",
        "                self.val[k] = 0\n",
        "                self.sum[k] = 0\n",
        "                self.count[k] = 0\n",
        "            self.val[k] = v\n",
        "            self.sum[k] += v * n\n",
        "            self.count[k] += n\n",
        "            self.avg[k] = self.sum[k] / self.count[k]\n",
        "class BaseDataModule:\n",
        "\n",
        "    @property\n",
        "    def train_dataset(self) ->paddle.io.Dataset:\n",
        "        raise NotImplementedError\n",
        "\n",
        "    @property\n",
        "    def val_dataset(self) ->paddle.io.Dataset:\n",
        "        raise NotImplementedError\n",
        "\n",
        "    @property\n",
        "    def test_dataset(self) ->paddle.io.Dataset:\n",
        "        raise NotImplementedError\n",
        "\n",
        "    def train_dataloader(self, **kwargs) ->paddle.io.DataLoader:\n",
        "        collate_fn = getattr(self, 'collate_fn', None)\n",
        "        return paddle.io.DataLoader(self.train_data, collate_fn=\n",
        "            collate_fn, **kwargs)\n",
        "\n",
        "    def val_dataloader(self, **kwargs) ->paddle.io.DataLoader:\n",
        "        collate_fn = getattr(self, 'collate_fn', None)\n",
        "        return paddle.io.DataLoader(self.val_data, collate_fn=\n",
        "            collate_fn, **kwargs)\n",
        "\n",
        "    def test_dataloader(self, **kwargs) ->paddle.io.DataLoader:\n",
        "        collate_fn = getattr(self, 'collate_fn', None)\n",
        "\n",
        "        return paddle.io.DataLoader(self.test_data, collate_fn=\n",
        "            collate_fn, **kwargs)\n",
        "\n",
        "\n",
        "def add_random_noise(data, noise_level=0.05, noise_type='gaussian'):\n",
        "    \"\"\"\n",
        "    Add random noise to the input data.\n",
        "\n",
        "    Args:\n",
        "    data (paddle.Tensor): Input data tensor\n",
        "    noise_level (float): The standard deviation of the noise (for Gaussian noise)\n",
        "                         or the scale of the noise (for uniform noise)\n",
        "    noise_type (str): Type of noise to add ('gaussian' or 'uniform')\n",
        "\n",
        "    Returns:\n",
        "    paddle.Tensor: Data with added noise\n",
        "    \"\"\"\n",
        "    if noise_type == 'gaussian':\n",
        "        noise = paddle.randn(data.shape) * noise_level\n",
        "    elif noise_type == 'uniform':\n",
        "        noise = (paddle.rand(data.shape) * 2 - 1) * noise_level\n",
        "    else:\n",
        "        raise ValueError(\"Unsupported noise type. Choose 'gaussian' or 'uniform'.\")\n",
        "\n",
        "    # Add noise to the data\n",
        "    noisy_data = data + noise\n",
        "\n",
        "    return noisy_data\n",
        "\n",
        "\n",
        "class RandomNoiseAugmentation(object):\n",
        "    def __init__(self, noise_level=0.05, noise_type='gaussian', prob=0.5):\n",
        "        self.noise_level = noise_level\n",
        "        self.noise_type = noise_type\n",
        "        self.prob = prob\n",
        "\n",
        "    def __call__(self, data_dict):\n",
        "        if np.random.rand() < self.prob:\n",
        "            data_dict['sdf'] = add_random_noise(data_dict['sdf'], self.noise_level, self.noise_type)\n",
        "        return data_dict\n",
        "\n",
        "\n",
        "class UnitGaussianNormalizer:\n",
        "    def __init__(self, x, eps=1e-05, reduce_dim=0, verbose=True):\n",
        "        super().__init__()\n",
        "        n_samples, *shape = x.shape\n",
        "        self.sample_shape = shape\n",
        "        self.verbose = verbose\n",
        "        self.reduce_dim = reduce_dim\n",
        "        y = x.numpy()\n",
        "        self.mean = paddle.to_tensor(\n",
        "            np.mean(y, axis=reduce_dim, keepdims=True).squeeze(axis=0)\n",
        "        )\n",
        "        self.std = paddle.to_tensor(\n",
        "            np.std(y, axis=reduce_dim, keepdims=True).squeeze(axis=0)\n",
        "        )\n",
        "        self.eps = eps\n",
        "        if verbose:\n",
        "            print(\n",
        "                f\"UnitGaussianNormalizer init on {n_samples}, reducing over {reduce_dim}, samples of shape {shape}.\"\n",
        "            )\n",
        "            print(f\"   Mean and std of shape {self.mean.shape}, eps={eps}\")\n",
        "\n",
        "    def encode(self, x):\n",
        "        x -= self.mean\n",
        "        x /= self.std + self.eps\n",
        "        return x\n",
        "\n",
        "    def decode(self, x, sample_idx=None):\n",
        "        if sample_idx is None:\n",
        "            std = self.std + self.eps\n",
        "            mean = self.mean\n",
        "        else:\n",
        "            if len(self.mean.shape) == len(sample_idx[0].shape):\n",
        "                std = self.std[sample_idx] + self.eps\n",
        "                mean = self.mean[sample_idx]\n",
        "            if len(self.mean.shape) > len(sample_idx[0].shape):\n",
        "                std = self.std[:, sample_idx] + self.eps\n",
        "                mean = self.mean[:, sample_idx]\n",
        "        x *= std\n",
        "        x += mean\n",
        "        return x\n",
        "\n",
        "    def cuda(self):\n",
        "        self.mean = self.mean\n",
        "        self.std = self.std\n",
        "        return self\n",
        "\n",
        "    def cpu(self):\n",
        "        self.mean = self.mean.cpu()\n",
        "        self.std = self.std.cpu()\n",
        "        return self\n",
        "\n",
        "    def to(self, device):\n",
        "        self.mean = self.mean.to(device)\n",
        "        self.std = self.std.to(device)\n",
        "        return self\n",
        "\n",
        "\n",
        "class PointGaussianNormalizer:\n",
        "    def __init__(self, x, eps=1e-05, reduce_dim=0, verbose=True):\n",
        "        super().__init__()\n",
        "        n_samples, *shape = x.shape\n",
        "        self.sample_shape = shape\n",
        "        self.verbose = verbose\n",
        "        self.reduce_dim = reduce_dim\n",
        "        y = x.numpy()\n",
        "        self.mean = paddle.to_tensor(\n",
        "            np.mean(y, axis=reduce_dim, keepdims=True)\n",
        "        )\n",
        "        self.std = paddle.to_tensor(\n",
        "            np.std(y, axis=reduce_dim, keepdims=True)\n",
        "        )\n",
        "        self.eps = eps\n",
        "        if verbose:\n",
        "            print(\n",
        "                f\"UnitGaussianNormalizer init on {n_samples}, reducing over {reduce_dim}, samples of shape {shape}.\"\n",
        "            )\n",
        "            print(f\"   Mean and std of shape {self.mean.shape}, eps={eps}\")\n",
        "\n",
        "    def encode(self, x):\n",
        "        x -= self.mean\n",
        "        x /= self.std + self.eps\n",
        "        return x\n",
        "\n",
        "    def decode(self, x, sample_idx=None):\n",
        "        if sample_idx is None:\n",
        "            std = self.std + self.eps\n",
        "            mean = self.mean\n",
        "        else:\n",
        "            if len(self.mean.shape) == len(sample_idx[0].shape):\n",
        "                std = self.std[sample_idx] + self.eps\n",
        "                mean = self.mean[sample_idx]\n",
        "            if len(self.mean.shape) > len(sample_idx[0].shape):\n",
        "                std = self.std[:, sample_idx] + self.eps\n",
        "                mean = self.mean[:, sample_idx]\n",
        "        x *= std\n",
        "        x += mean\n",
        "        return x\n",
        "\n",
        "    def cuda(self):\n",
        "        self.mean = self.mean\n",
        "        self.std = self.std\n",
        "        return self\n",
        "\n",
        "    def cpu(self):\n",
        "        self.mean = self.mean.cpu()\n",
        "        self.std = self.std.cpu()\n",
        "        return self\n",
        "\n",
        "    def to(self, device):\n",
        "        self.mean = self.mean.to(device)\n",
        "        self.std = self.std.to(device)\n",
        "        return self\n",
        "\n",
        "\n",
        "\n",
        "class DictDataset(paddle.io.Dataset):\n",
        "    def __init__(self, data_dict: dict):\n",
        "        self.data_dict = data_dict\n",
        "        # for k, v in data_dict.items():\n",
        "        #     assert len(v) == len(\n",
        "        #         data_dict[list(data_dict.keys())[0]]\n",
        "        #     ), \"All data must have the same length\" + f\"Key: {k} : {v.shape}\"\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        return {k: v[index] for k, v in self.data_dict.items()}\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data_dict[list(self.data_dict.keys())[0]])\n",
        "\n",
        "\n",
        "class VerticeEdgeDataset(paddle.io.Dataset):\n",
        "    def __init__(self, vertices: np.ndarray, edges: np.ndarray, pressure: np.ndarray = None):\n",
        "        self.vertices = vertices.astype(np.float32)\n",
        "        self.edges = edges\n",
        "        self.pressure = pressure.astype(np.float32)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        return self.vertices[index], self.edges[index], self.pressure[index]\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.vertices)\n",
        "\n",
        "\n",
        "class DictDatasetWithConstant(DictDataset):\n",
        "    def __init__(self, data_dict: dict, constant_dict: dict):\n",
        "        super().__init__(data_dict)\n",
        "        self.constant_dict = constant_dict\n",
        "        self.lazy_load_data = True if \"data_module\" in constant_dict else False\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        return_dict = {k: v[index] for k, v in self.data_dict.items()}\n",
        "        return_dict.update(self.constant_dict)\n",
        "\n",
        "        if self.lazy_load_data is True:\n",
        "            data_dir = self.constant_dict[\"data_dir\"]\n",
        "            data_module = self.constant_dict[\"data_module\"]\n",
        "            mesh_index = self.constant_dict[\"mesh_index\"][index]\n",
        "\n",
        "            if \"test\" in str(data_dir):\n",
        "                pass\n",
        "            else:\n",
        "                # only for train and validation, we load pressure\n",
        "                # load [p]\n",
        "                print(data_dir)\n",
        "                p = data_module.load_pressure(data_dir, \"\", mesh_index)\n",
        "                p = paddle.to_tensor(data=p, dtype=\"float32\")\n",
        "\n",
        "                # encode [p]\n",
        "                encode = data_module.pressure_normalization.encode\n",
        "                p = encode(p)\n",
        "                return_dict[\"pressure\"] = p\n",
        "\n",
        "            # load [centroid]\n",
        "            centroid = data_module.load_centroid(data_dir, \"\", mesh_index)\n",
        "            centroid = paddle.to_tensor(data=centroid)\n",
        "\n",
        "            # normalized [centroid]\n",
        "            centroid = data_module.location_normalization(centroid, data_module.min_bounds, data_module.max_bounds)\n",
        "            return_dict[\"vertices\"] = centroid  # It should be centroid, not vertices, TODO: fix it\n",
        "        # TODO: need more efficient way\n",
        "        return return_dict\n",
        "\n",
        "    def convert_dict_to_numpy(self, d):\n",
        "\n",
        "        new_dict = {}\n",
        "        for k, v in d.items():\n",
        "            if isinstance(v, paddle.Tensor):\n",
        "                new_dict[k] = v.numpy()\n",
        "            else:\n",
        "                new_dict[k] = v\n",
        "        return new_dict\n",
        "\n",
        "    def lazy_load(self, index, return_dict):\n",
        "        \"\"\"\n",
        "        Lazy loading function for pressure and centroid data.\n",
        "\n",
        "        Args:\n",
        "            index (int): The index of the mesh to be loaded.\n",
        "            return_dict (dict): A dictionary to store the loaded data.\n",
        "\n",
        "        Returns:\n",
        "            dict: A dictionary containing the loaded pressure and centroid data.\n",
        "\n",
        "        \"\"\"\n",
        "\n",
        "        return return_dict\n",
        "\n",
        "    def decode(self, output):\n",
        "        assert \"data_module\" in self.constant_dict\n",
        "        return self.constant_dict[\"data_module\"].pressure_normalization.decode(output)\n",
        "\n",
        "\n",
        "class CFDDataModule(BaseDataModule):\n",
        "    def __init__(self, data_dir, n_train: int = 500, n_test: int = 111):\n",
        "        super().__init__()\n",
        "        if isinstance(data_dir, str):\n",
        "            data_dir = Path(data_dir)\n",
        "        data_dir = data_dir.expanduser()\n",
        "        assert data_dir.exists(), \"Path does not exist\"\n",
        "        assert data_dir.is_dir(), \"Path is not a directory\"\n",
        "        self.data_dir = data_dir\n",
        "        valid_mesh_inds = self.load_valid_mesh_indices(data_dir)\n",
        "        assert n_train + n_test <= len(valid_mesh_inds), \"Not enough data\"\n",
        "        if n_train + n_test < len(valid_mesh_inds):\n",
        "            warnings.warn(\n",
        "                f\"{len(valid_mesh_inds)} meshes are available, but {n_train + n_test} are requested.\"\n",
        "            )\n",
        "        train_indices = valid_mesh_inds[:n_train]\n",
        "        test_indices = valid_mesh_inds[-n_test:]\n",
        "        train_mesh_paths = [self.get_mesh_path(data_dir, i) for i in train_indices]\n",
        "        test_mesh_paths = [self.get_mesh_path(data_dir, i) for i in test_indices]\n",
        "        train_vertices = [\n",
        "            self.vertices_from_mesh(mesh_path) for mesh_path in train_mesh_paths\n",
        "        ]\n",
        "        test_vertices = [\n",
        "            self.vertices_from_mesh(mesh_path) for mesh_path in test_mesh_paths\n",
        "        ]\n",
        "        train_pressure = paddle.stack(\n",
        "            x=[\n",
        "                paddle.to_tensor(\n",
        "                    data=self.load_pressure(data_dir, mesh_index), dtype=\"float32\"\n",
        "                )\n",
        "                for mesh_index in train_indices\n",
        "            ]\n",
        "        )\n",
        "        test_pressure = paddle.stack(\n",
        "            x=[\n",
        "                paddle.to_tensor(\n",
        "                    data=self.load_pressure(data_dir, mesh_index), dtype=\"float32\"\n",
        "                )\n",
        "                for mesh_index in test_indices\n",
        "            ]\n",
        "        )\n",
        "        pressure_normalization = UnitGaussianNormalizer(\n",
        "            train_pressure, eps=1e-06, reduce_dim=0, verbose=False\n",
        "        )\n",
        "        # train_pressure = pressure_normalization.encode(train_pressure)\n",
        "        # test_pressure = pressure_normalization.encode(test_pressure)\n",
        "        self._train_data = DictDataset(\n",
        "            {\"vertices\": train_vertices, \"pressure\": train_pressure}\n",
        "        )\n",
        "        self._test_data = DictDataset(\n",
        "            {\"vertices\": test_vertices, \"pressure\": test_pressure}\n",
        "        )\n",
        "        self.output_normalization = pressure_normalization\n",
        "\n",
        "    def encode(self, pressure: paddle.Tensor) -> paddle.Tensor:\n",
        "        return self.output_normalization.encode(pressure)\n",
        "\n",
        "    def decode(self, ouput: paddle.Tensor) -> paddle.Tensor:\n",
        "        # output.shape = [batch_size, points_number, 1]\n",
        "        pressure = ouput[0, :, 0].reshape((-1, 1))\n",
        "        pressure_decode = self.output_normalization[0].decode(pressure)\n",
        "        if len(self.output_normalization) == 2:\n",
        "            wss = ouput[:, 1].reshape((-1, 1))\n",
        "            wss_decode = self.output_normalization[1].decode(wss)\n",
        "            return paddle.concat([pressure_decode, wss_decode], axis=1)\n",
        "        return pressure_decode\n",
        "\n",
        "    @property\n",
        "    def train_data(self):\n",
        "        return self._train_data\n",
        "\n",
        "    @property\n",
        "    def test_data(self):\n",
        "        return self._test_data\n",
        "\n",
        "    def vertices_from_mesh(self, mesh_path: Path) -> paddle.Tensor:\n",
        "        mesh = self.load_mesh(mesh_path)\n",
        "        vertices = mesh.vertex.positions.numpy()\n",
        "        return vertices\n",
        "\n",
        "    def get_mesh_path(self, data_dir: Path, mesh_ind: int) -> Path:\n",
        "        return data_dir / (\"mesh_\" + str(mesh_ind).zfill(3) + \".ply\")\n",
        "\n",
        "    def get_pressure_data_path(self, data_dir: Path, mesh_ind: int) -> Path:\n",
        "        return data_dir / (\"press_\" + str(mesh_ind).zfill(3) + \".npy\")\n",
        "\n",
        "    def load_pressure(self, data_dir: Path, mesh_index: int) -> np.ndarray:\n",
        "        press_path = self.get_pressure_data_path(data_dir, mesh_index)\n",
        "        assert press_path.exists(), \"Pressure data does not exist\"\n",
        "        press = np.load(press_path).reshape((-1,)).astype(np.float32)\n",
        "        press = np.concatenate((press[0:16], press[112:]), axis=0)\n",
        "        return press\n",
        "\n",
        "    def load_valid_mesh_indices(\n",
        "            self, data_dir, filename=\"watertight_meshes.txt\"\n",
        "    ) -> List[int]:\n",
        "        with open(data_dir / filename, \"r\") as fp:\n",
        "            mesh_ind = fp.read().split(\"\\n\")\n",
        "            mesh_ind = [int(a) for a in mesh_ind]\n",
        "        return mesh_ind\n",
        "\n",
        "    def load_mesh(self, mesh_path: Path):\n",
        "        assert mesh_path.exists(), \"Mesh path does not exist\"\n",
        "        mesh = o3d.io.read_triangle_mesh(str(mesh_path))\n",
        "        # mesh = o3d.t.geometry.TriangleMesh.from_legacy(mesh)\n",
        "        return mesh\n",
        "\n",
        "    def load_mesh_from_index(\n",
        "            self, data_dir, mesh_index: int\n",
        "    ) -> o3d.t.geometry.TriangleMesh:\n",
        "        mesh_path = self.get_mesh_path(data_dir, mesh_index)\n",
        "        return self.load_mesh(mesh_path)\n",
        "\n",
        "\n",
        "class CFDSDFDataModule(CFDDataModule):\n",
        "    def __init__(\n",
        "            self,\n",
        "            data_dir,\n",
        "            n_train: int = 10,\n",
        "            n_test: int = 5,\n",
        "            spatial_resolution: Tuple[int, int, int] = None,\n",
        "            query_points=None,\n",
        "            eps=0.01,\n",
        "            closest_points_to_query=True,\n",
        "            test_data_dir=None,\n",
        "            use_aug_feature=False,\n",
        "            rag_data_dir: str = None,\n",
        "    ):\n",
        "        BaseDataModule.__init__(self)\n",
        "\n",
        "        if isinstance(data_dir, str):\n",
        "            data_dir = Path(data_dir)\n",
        "            test_data_dir = Path(test_data_dir)\n",
        "\n",
        "        data_dir = data_dir.expanduser()\n",
        "        test_data_dir = test_data_dir.expanduser()\n",
        "        assert data_dir.exists(), \"Path does not exist\"\n",
        "        assert data_dir.is_dir(), \"Path is not a directory\"\n",
        "        self.data_dir = data_dir\n",
        "        self.test_data_dir = test_data_dir\n",
        "\n",
        "        min_bounds, max_bounds = self.load_bound(data_dir, eps=eps)\n",
        "        valid_mesh_inds_train = self.load_valid_mesh_indices(data_dir)\n",
        "        valid_mesh_inds_test = self.load_valid_mesh_indices(test_data_dir)\n",
        "        valid_mesh_inds = valid_mesh_inds_train + valid_mesh_inds_test\n",
        "        assert n_train + n_test <= len(valid_mesh_inds), \"Not enough data\"\n",
        "        if n_train + n_test < len(valid_mesh_inds):\n",
        "            warnings.warn(\n",
        "                f\"{len(valid_mesh_inds)} meshes are available, but {n_train + n_test} are requested.\"\n",
        "            )\n",
        "\n",
        "        train_indices = valid_mesh_inds_train[:n_train]\n",
        "        test_indices = valid_mesh_inds_test[:n_test]\n",
        "        train_mesh_paths = [self.get_mesh_path(data_dir, i) for i in train_indices]\n",
        "        test_mesh_paths = [self.get_mesh_path(test_data_dir, i) for i in test_indices]\n",
        "        self.test_mesh_paths = test_mesh_paths\n",
        "        self.test_indices = test_indices\n",
        "        #\n",
        "        # train_aug_vertices, rag_pressures_train = self.read_data(os.path.join(rag_data_dir, \"rag_train\"))\n",
        "        # test_aug_vertices, rag_pressures_test = self.read_data(os.path.join(rag_data_dir, \"rag_test_A\"))\n",
        "\n",
        "        # TODO: load test data\n",
        "\n",
        "        if query_points is None:\n",
        "            assert spatial_resolution is not None, \"spatial_resolution must be given\"\n",
        "            tx = np.linspace(min_bounds[0], max_bounds[0], spatial_resolution[0])\n",
        "            ty = np.linspace(min_bounds[1], max_bounds[1], spatial_resolution[1])\n",
        "            tz = np.linspace(min_bounds[2], max_bounds[2], spatial_resolution[2])\n",
        "            query_points = np.stack(\n",
        "                np.meshgrid(tx, ty, tz, indexing=\"ij\"), axis=-1\n",
        "            ).astype(np.float32)\n",
        "\n",
        "        train_sdf_mesh_vertices = [\n",
        "            self.sdf_vertices_closest_from_mesh(\n",
        "                mesh_path, query_points, closest_points_to_query, use_aug_feature\n",
        "            )\n",
        "            for mesh_path in train_mesh_paths\n",
        "        ]\n",
        "\n",
        "        train_sdf = paddle.stack(\n",
        "            x=[paddle.to_tensor(data=sdf) for sdf, _, _, _, _ in train_sdf_mesh_vertices]\n",
        "        )\n",
        "        train_vertices = paddle.stack(\n",
        "            x=[\n",
        "                paddle.to_tensor(data=vertices)\n",
        "                for _, vertices, _, _, _ in train_sdf_mesh_vertices\n",
        "            ]\n",
        "        )\n",
        "        train_edges = paddle.stack(\n",
        "            x=[\n",
        "                paddle.to_tensor(data=edges)\n",
        "                for _, _, _, edges, _ in train_sdf_mesh_vertices\n",
        "            ]\n",
        "        )\n",
        "\n",
        "\n",
        "        if closest_points_to_query:\n",
        "            train_closest_points = paddle.stack(\n",
        "                x=[\n",
        "                    paddle.to_tensor(data=closest)\n",
        "                    for _, _, closest, _, _ in train_sdf_mesh_vertices\n",
        "                ]\n",
        "            )\n",
        "        else:\n",
        "            train_closest_points = None\n",
        "\n",
        "        del train_sdf_mesh_vertices\n",
        "        train_pressure = paddle.stack(\n",
        "            x=[\n",
        "                paddle.to_tensor(\n",
        "                    data=self.load_pressure(data_dir, mesh_index), dtype=\"float32\"\n",
        "                )\n",
        "                for mesh_index in train_indices\n",
        "            ]\n",
        "        )\n",
        "        min_bounds = paddle.to_tensor(data=min_bounds)\n",
        "        max_bounds = paddle.to_tensor(data=max_bounds)\n",
        "        train_vertices[:, :, :3] = self.location_normalization(\n",
        "            train_vertices[:, :, :3], min_bounds, max_bounds\n",
        "        )\n",
        "        if closest_points_to_query:\n",
        "            train_closest_points = self.location_normalization(\n",
        "                train_closest_points, min_bounds, max_bounds\n",
        "            ).transpose(perm=[0, 4, 1, 2, 3])\n",
        "        test_sdf_mesh_vertices = [\n",
        "            self.sdf_vertices_closest_from_mesh(\n",
        "                mesh_path, query_points, closest_points_to_query\n",
        "            )\n",
        "            for mesh_path in test_mesh_paths\n",
        "        ]\n",
        "\n",
        "        test_sdf = paddle.stack(\n",
        "            x=[paddle.to_tensor(data=sdf) for sdf, _, _, _, _ in test_sdf_mesh_vertices]\n",
        "        )\n",
        "\n",
        "        test_vertices = paddle.stack(\n",
        "            x=[\n",
        "                paddle.to_tensor(data=vertices)\n",
        "                for _, vertices, _, _, _ in test_sdf_mesh_vertices\n",
        "            ]\n",
        "        )\n",
        "        test_edges = paddle.stack(\n",
        "            x=[\n",
        "                paddle.to_tensor(data=edges)\n",
        "                for _, _, _, edges, _ in test_sdf_mesh_vertices\n",
        "            ]\n",
        "        )\n",
        "\n",
        "        if closest_points_to_query:\n",
        "            test_closest_points = paddle.stack(\n",
        "                x=[\n",
        "                    paddle.to_tensor(data=closest)\n",
        "                    for _, _, closest, _, _ in test_sdf_mesh_vertices\n",
        "                ]\n",
        "            )\n",
        "        else:\n",
        "            test_closest_points = None\n",
        "        del test_sdf_mesh_vertices\n",
        "\n",
        "        self.train_mesh_paths = train_mesh_paths\n",
        "        self.train_indices = train_indices\n",
        "        # fake test pressure\n",
        "        if (test_data_dir.name == 'data_test_A') or (test_data_dir.name == 'data_test_B'):\n",
        "            print(f\"fake pressure for {test_data_dir.name}\")\n",
        "            for v, mesh_index in zip(test_vertices, test_indices):\n",
        "                p_length = v.shape[0] + 96  # 96 = 112 - 16 for pressure truncation\n",
        "                p_test_fake = np.ones((p_length,), dtype=np.float32)\n",
        "                np.save(self.get_pressure_data_path(test_data_dir, mesh_index), p_test_fake)\n",
        "\n",
        "        test_pressure = paddle.stack(\n",
        "            x=[\n",
        "                paddle.to_tensor(\n",
        "                    data=self.load_pressure(test_data_dir, mesh_index), dtype=\"float32\"\n",
        "                )\n",
        "                for mesh_index in test_indices\n",
        "            ]\n",
        "        )\n",
        "        test_vertices[:, :, :3] = self.location_normalization(\n",
        "            test_vertices[:, :, :3], min_bounds, max_bounds\n",
        "        )\n",
        "        if closest_points_to_query:\n",
        "            test_closest_points = self.location_normalization(\n",
        "                test_closest_points, min_bounds, max_bounds\n",
        "            ).transpose(perm=[0, 4, 1, 2, 3])\n",
        "        # pressure_normalization = UnitGaussianNormalizer(\n",
        "        #     train_pressure, eps=1e-06, reduce_dim=0, verbose=False\n",
        "        # )\n",
        "        pressure_normalization = UnitGaussianNormalizer(\n",
        "            train_pressure, eps=1e-06, reduce_dim=(0, 1), verbose=False\n",
        "        )\n",
        "        mean, std = self.load_bound(\n",
        "            data_dir, filename=\"train_pressure_min_std.txt\", eps=0.0\n",
        "        )\n",
        "        pressure_normalization.mean, pressure_normalization.std = paddle.to_tensor([mean[0]]), paddle.to_tensor(\n",
        "            [std[0]])\n",
        "\n",
        "        train_pressure = pressure_normalization.encode(train_pressure)\n",
        "        test_pressure = pressure_normalization.encode(test_pressure)\n",
        "        normalized_query_points = self.location_normalization(\n",
        "            paddle.to_tensor(data=query_points), min_bounds, max_bounds\n",
        "        ).transpose(perm=[3, 0, 1, 2])\n",
        "\n",
        "        train_aug_vertices =  ( train_vertices[:, :, 3]*10 + 10) ** 2  # 500, 3586, 30\n",
        "        train_aug_vertices =  (( train_vertices[:, :, 2] - 10) * 10)    # 500, 3586, 30\n",
        "        test_aug_vertices=  ( test_vertices[:, :, 3]*10 + 10) ** 2  # 500, 3586, 30\n",
        "        test_aug_vertices =  (( test_vertices[:, :, 2] - 10) * 10)    # 500, 3586, 30\n",
        "        self._train_data = DictDatasetWithConstant(\n",
        "            {\"sdf\": train_sdf, \"vertices\": train_vertices, \"pressure\": train_pressure, \"edges\": train_edges,\n",
        "             \"aug_features\": train_aug_vertices},\n",
        "            {\"sdf_query_points\": normalized_query_points},\n",
        "        )\n",
        "\n",
        "        self._test_data = DictDatasetWithConstant(\n",
        "            {\"sdf\": test_sdf, \"vertices\": test_vertices, \"pressure\": test_pressure, \"edges\": test_edges,\n",
        "             \"aug_features\": test_aug_vertices},\n",
        "            {\"sdf_query_points\": normalized_query_points},\n",
        "        )\n",
        "        if closest_points_to_query:\n",
        "            self._train_data.data_dict[\"closest_points\"] = train_closest_points\n",
        "            self._test_data.data_dict[\"closest_points\"] = test_closest_points\n",
        "        self.output_normalization = [pressure_normalization, ]\n",
        "\n",
        "    def load_bound(\n",
        "            self, data_dir, filename=\"watertight_global_bounds.txt\", eps=1e-06\n",
        "    ) -> Tuple[List[float], List[float]]:\n",
        "        with open(data_dir / filename, \"r\") as fp:\n",
        "            min_bounds = fp.readline().split(\" \")\n",
        "            max_bounds = fp.readline().split(\" \")\n",
        "            min_bounds = [(float(a) - eps) for a in min_bounds]\n",
        "            max_bounds = [(float(a) + eps) for a in max_bounds]\n",
        "        return min_bounds, max_bounds\n",
        "\n",
        "    def location_normalization(\n",
        "            self,\n",
        "            locations: paddle.Tensor,\n",
        "            min_bounds: paddle.Tensor,\n",
        "            max_bounds: paddle.Tensor,\n",
        "    ) -> paddle.Tensor:\n",
        "        \"\"\"\n",
        "        Normalize locations to [-1, 1].\n",
        "        \"\"\"\n",
        "        locations = (locations - min_bounds) / (max_bounds - min_bounds)\n",
        "        locations = 2 * locations - 1\n",
        "        return locations\n",
        "\n",
        "    def compute_sdf(\n",
        "            self, mesh: Union[Path, o3d.t.geometry.TriangleMesh], query_points\n",
        "    ) -> np.ndarray:\n",
        "        if isinstance(mesh, Path):\n",
        "            mesh = self.mesh_to_tmesh(self.load_mesh(mesh))\n",
        "        scene = o3d.t.geometry.RaycastingScene()\n",
        "        _ = scene.add_triangles(mesh)\n",
        "        signed_distance = scene.compute_signed_distance(query_points).numpy()\n",
        "        return signed_distance\n",
        "\n",
        "    def closest_points_to_query_from_mesh(\n",
        "            self, mesh: o3d.t.geometry.TriangleMesh, query_points\n",
        "    ) -> np.ndarray:\n",
        "        scene = o3d.t.geometry.RaycastingScene()\n",
        "        _ = scene.add_triangles(mesh)\n",
        "        closest_points = scene.compute_closest_points(query_points)[\"points\"].numpy()\n",
        "        return closest_points\n",
        "\n",
        "    def sdf_vertices_closest_from_mesh(\n",
        "            self, mesh_path: Path, query_points: np.ndarray, closest_points: bool, aug_feature: bool = None\n",
        "    ) -> Tuple[np.ndarray, np.ndarray, Union[np.ndarray, None], np.ndarray, np.ndarray]:\n",
        "        mesh = self.load_mesh(mesh_path)\n",
        "        t_mesh = self.mesh_to_tmesh(mesh)\n",
        "        if aug_feature:\n",
        "            aug_features = self.vertices_augmented_from_mesh(t_mesh)\n",
        "        else:\n",
        "            aug_features = np.array([0])\n",
        "        sdf = self.compute_sdf(t_mesh, query_points)\n",
        "        vertices = np.asarray(mesh.vertices)\n",
        "        vertices_wind_weights = self.get_wind_weights(mesh)\n",
        "        vertices = np.concatenate([vertices, vertices_wind_weights[:, None]], axis=-1).astype(np.float32)\n",
        "        edge_index = self.create_graph_from_mesh(mesh)\n",
        "        if closest_points:\n",
        "            closest_points = self.closest_points_to_query_from_mesh(t_mesh, query_points)\n",
        "        else:\n",
        "            closest_points = None\n",
        "        # todo\n",
        "        return sdf, vertices, closest_points, edge_index, aug_features\n",
        "\n",
        "    def get_wind_weights(self, mesh):\n",
        "        mesh.compute_vertex_normals()\n",
        "        # 设定风向,这里假设沿z轴方向 todo: along z direction\n",
        "        wind_direction = np.array([0, 0, -1])\n",
        "        face_normals = np.asarray(mesh.triangle_normals)\n",
        "        face_angles = np.dot(face_normals, wind_direction) / (\n",
        "                np.linalg.norm(face_normals, axis=1) * np.linalg.norm(wind_direction))\n",
        "\n",
        "        # 初始化每个顶点的权重为0\n",
        "        vertex_count = len(mesh.vertices)\n",
        "        vertex_weights = np.zeros(vertex_count)\n",
        "        triangles = np.asarray(mesh.triangles)\n",
        "        for i, triangle in enumerate(triangles):\n",
        "            for vertex_index in triangle:\n",
        "                vertex_weights[vertex_index] += face_angles[i]\n",
        "        vertex_weights = vertex_weights * 10\n",
        "        # # 可视化带有权重的点云，权重映射到颜色上\n",
        "        # max_weight = np.max(vertex_weights)\n",
        "        # colors = np.zeros((vertex_count, 3))\n",
        "        # colors[:, 0] = vertex_weights / max_weight  # 使用权重映射到红色通道\n",
        "        # # 创建一个点云对象来可视化这些点\n",
        "        # vertex_points = np.asarray(mesh.vertices)\n",
        "        # weighted_pcd = o3d.geometry.PointCloud()\n",
        "        # weighted_pcd.points = o3d.utility.Vector3dVector(vertex_points)\n",
        "        # weighted_pcd.colors = o3d.utility.Vector3dVector(colors)\n",
        "        #\n",
        "        # #可视化\n",
        "        # o3d.visualization.draw_geometries([mesh, weighted_pcd])\n",
        "        return vertex_weights\n",
        "\n",
        "    def create_graph_from_mesh(self, mesh):\n",
        "        edges = []\n",
        "        for triangle in np.asarray(mesh.triangles):\n",
        "            edges.extend([(triangle[0], triangle[1]),\n",
        "                          (triangle[1], triangle[2]),\n",
        "                          (triangle[2], triangle[0])])\n",
        "\n",
        "        edges = list(set(edges))\n",
        "        edge_index = np.array(edges, dtype=np.int64).T\n",
        "\n",
        "        return edge_index\n",
        "\n",
        "    def mesh_to_tmesh(self, mesh: o3d.geometry.TriangleMesh) -> o3d.t.geometry.TriangleMesh:\n",
        "        mesh = o3d.t.geometry.TriangleMesh.from_legacy(mesh)\n",
        "        return mesh\n",
        "\n",
        "    def vertices_augmented_from_mesh(self, mesh: o3d.t.geometry.TriangleMesh, slices=10) -> np.ndarray:\n",
        "        vertices = mesh.vertex.positions.numpy()\n",
        "        mesh = mesh.compute_vertex_normals()\n",
        "        mesh = mesh.compute_triangle_normals()\n",
        "        normals = mesh.vertex.normals.numpy()\n",
        "\n",
        "        x_signs = np.sign(normals[:, 0:1])\n",
        "        y_signs = np.sign(normals[:, 1:2])\n",
        "        z_signs = np.sign(normals[:, 2:3])\n",
        "        cosx = normals * np.array([1, 0, 0])\n",
        "        cosy = normals * np.array([0, 1, 0])\n",
        "        cosz = normals * np.array([0, 0, 1])\n",
        "        sinx = np.sqrt(1 - cosx ** 2)\n",
        "        siny = np.sqrt(1 - cosy ** 2)\n",
        "        sinz = np.sqrt(1 - cosz ** 2)\n",
        "\n",
        "        x_min, x_max = vertices[:, 0].min(), vertices[:, 0].max()\n",
        "        y_min, y_max = vertices[:, 1].min(), vertices[:, 1].max()\n",
        "        z_min, z_max = vertices[:, 2].min(), vertices[:, 2].max()\n",
        "        x_slices = np.linspace(x_min, x_max, slices)\n",
        "        y_slices = np.linspace(y_min, y_max, slices)\n",
        "        z_slices = np.linspace(z_min, z_max, slices)\n",
        "        x_areas_linspace, y_areas_linspace, z_areas_linspace = np.zeros((slices, 1)), np.zeros((slices, 1)), np.zeros(\n",
        "            (slices, 1))\n",
        "        o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Error)\n",
        "        for i in range(slices):\n",
        "            x_lineset = mesh.slice_plane([x_slices[i], 0, 0], [1, 0, 0]).point.positions.numpy()\n",
        "            y_lineset = mesh.slice_plane([0, y_slices[i], 0], [0, 1, 0]).point.positions.numpy()\n",
        "            z_lineset = mesh.slice_plane([0, 0, z_slices[i]], [0, 0, 1]).point.positions.numpy()\n",
        "\n",
        "            # calculate shoelace formula\n",
        "            x_areas_linspace[i, 0] = 0.5 * np.abs(\n",
        "                np.sum(np.dot(x_lineset[:-1, 1], x_lineset[1:, 2]) - np.dot(x_lineset[1:, 1], x_lineset[:-1, 2])))\n",
        "            y_areas_linspace[i, 0] = 0.5 * np.abs(\n",
        "                np.sum(np.dot(y_lineset[:-1, 0], y_lineset[1:, 2]) - np.dot(y_lineset[1:, 0], y_lineset[:-1, 2])))\n",
        "            z_areas_linspace[i, 0] = 0.5 * np.abs(\n",
        "                np.sum(np.dot(z_lineset[:-1, 0], z_lineset[1:, 1]) - np.dot(z_lineset[1:, 0], z_lineset[:-1, 1])))\n",
        "\n",
        "        x_areas_index = (vertices[:, 0] - x_min) / (x_max - x_min) * (slices - 1)\n",
        "        x_areas = x_areas_linspace[x_areas_index.astype(int)]\n",
        "        y_areas_index = (vertices[:, 1] - y_min) / (y_max - y_min) * (slices - 1)\n",
        "        y_areas = y_areas_linspace[y_areas_index.astype(int)]\n",
        "        z_areas_index = (vertices[:, 2] - z_min) / (z_max - z_min) * (slices - 1)\n",
        "        z_areas = z_areas_linspace[z_areas_index.astype(int)]\n",
        "\n",
        "        aug_features = np.concatenate(\n",
        "            [normals, x_signs, y_signs, z_signs, cosx, cosy, cosz, sinx, siny, sinz, x_areas, y_areas,\n",
        "             z_areas], axis=1)\n",
        "\n",
        "        return aug_features.astype(np.float32)\n",
        "\n",
        "    def read_data(self, data_dir: str, topK: int = 2):\n",
        "        assert topK > 1\n",
        "        if isinstance(data_dir, str):\n",
        "            data_dir = Path(data_dir)\n",
        "        data_dir = data_dir.expanduser()\n",
        "        with open(data_dir / 'watertight_meshes.txt', \"r\") as fp:\n",
        "            mesh_ind = fp.read().split(\"\\n\")\n",
        "            mesh_ind = [int(a) for a in mesh_ind]\n",
        "\n",
        "        def get_mesh_path(data_dir: Path, mesh_ind: int) -> Path:\n",
        "            return data_dir / (\"mesh_\" + str(mesh_ind) + \".npy\")\n",
        "\n",
        "        meshes = []\n",
        "        rag_pressures_origin = np.load(data_dir / 'rag_pressure.npy')  # [500, 500, 3586]\n",
        "        distances = np.load(data_dir / 'distances.npy')  # [500, 50]\n",
        "        topK_indices = np.argpartition(distances, topK, axis=0)[topK - 1]\n",
        "        rag_pressures = []\n",
        "        for i, index in enumerate(topK_indices):\n",
        "            rag_pressures.append(rag_pressures_origin[index][i])\n",
        "        rag_pressures = np.stack(rag_pressures, axis=0)\n",
        "        for ind in mesh_ind:\n",
        "            mesh_path = get_mesh_path(data_dir, ind)\n",
        "            meshes.append(np.load(mesh_path))\n",
        "        meshes = np.stack(meshes, axis=0)\n",
        "        return meshes.astype(np.float32), rag_pressures.astype(np.float32)  # [500, 3586, 3], [500, 3586], [500, 3586]\n",
        "\n",
        "\n",
        "class CarDataModule(CFDSDFDataModule):\n",
        "    def __init__(\n",
        "            self,\n",
        "            data_dir,\n",
        "            test_data_dir,\n",
        "            n_train: int = 1,\n",
        "            n_test: int = 1,\n",
        "            spatial_resolution: Tuple[int, int, int] = None,\n",
        "            query_points=None,\n",
        "            eps=0.01,\n",
        "            closest_points_to_query=True,\n",
        "    ):\n",
        "        BaseDataModule.__init__(self)\n",
        "        if isinstance(data_dir, str):\n",
        "            data_dir = Path(data_dir)\n",
        "            test_data_dir = Path(test_data_dir)\n",
        "        data_dir = data_dir.expanduser()\n",
        "        test_data_dir = test_data_dir.expanduser()\n",
        "        assert data_dir.exists(), \"Path does not exist\"\n",
        "        assert data_dir.is_dir(), \"Path is not a directory\"\n",
        "        self.data_dir = data_dir\n",
        "        self.test_data_dir = test_data_dir\n",
        "        min_bounds, max_bounds = self.load_bound(\n",
        "            data_dir, filename=\"global_bounds.txt\", eps=eps\n",
        "        )\n",
        "        min_info_bounds, max_info_bounds = self.load_bound(\n",
        "            test_data_dir, filename=\"info_bounds.txt\", eps=0.0\n",
        "        )\n",
        "        min_area_bound, max_area_bound = self.load_bound(\n",
        "            test_data_dir, filename='area_bounds.txt', eps=0.0\n",
        "        )\n",
        "        assert n_train <= 500, \"Not enough training data\"\n",
        "        assert n_test <= 51, \"Not enough testing data\"\n",
        "        if n_train + n_test < 551:\n",
        "            warnings.warn(\n",
        "                f\"551 meshes are available, but {n_train + n_test} are requested.\"\n",
        "            )\n",
        "        train_indices = np.loadtxt(data_dir / \"train_index.txt\", dtype=int)\n",
        "        train_indices = train_indices[:n_train]\n",
        "\n",
        "        test_indices = [(j + 1) for j in range(n_test)]\n",
        "        self.test_indices = test_indices\n",
        "        train_mesh_paths = [\n",
        "            self.get_mesh_path(data_dir, \"\", i) for i in train_indices\n",
        "        ]\n",
        "        test_mesh_paths = [\n",
        "            self.get_mesh_path(test_data_dir, \"\", i) for i in test_indices\n",
        "        ]\n",
        "        self.test_mesh_paths = test_mesh_paths\n",
        "\n",
        "        if query_points is None:\n",
        "            assert spatial_resolution is not None, \"spatial_resolution must be given\"\n",
        "            tx = np.linspace(min_bounds[0], max_bounds[0], spatial_resolution[0])\n",
        "            ty = np.linspace(min_bounds[1], max_bounds[1], spatial_resolution[1])\n",
        "            tz = np.linspace(min_bounds[2], max_bounds[2], spatial_resolution[2])\n",
        "            query_points = np.stack(\n",
        "                np.meshgrid(tx, ty, tz, indexing=\"ij\"), axis=-1\n",
        "            ).astype(np.float32)\n",
        "        train_df_closest = [\n",
        "            self.df_from_mesh(mesh_path, query_points, closest_points_to_query)\n",
        "            for mesh_path in train_mesh_paths\n",
        "        ]\n",
        "        train_sdf = paddle.stack(\n",
        "            x=[paddle.to_tensor(data=df) for df, _ in train_df_closest]\n",
        "        )\n",
        "\n",
        "        if closest_points_to_query:\n",
        "            train_closest_points = paddle.stack(\n",
        "                x=[paddle.to_tensor(data=closest) for _, closest in train_df_closest]\n",
        "            )\n",
        "        else:\n",
        "            train_closest_points = None\n",
        "\n",
        "        del train_df_closest\n",
        "        test_df_closest = [\n",
        "            self.df_from_mesh(mesh_path, query_points, closest_points_to_query)\n",
        "            for mesh_path in test_mesh_paths\n",
        "        ]\n",
        "        test_sdf = paddle.stack(\n",
        "            x=[paddle.to_tensor(data=df) for df, _ in test_df_closest]\n",
        "        )\n",
        "\n",
        "        if closest_points_to_query:\n",
        "            test_closest_points = paddle.stack(\n",
        "                x=[paddle.to_tensor(data=closest) for _, closest in test_df_closest]\n",
        "            )\n",
        "        else:\n",
        "            test_closest_points = None\n",
        "        del test_df_closest\n",
        "        # fake pressure for normalization\n",
        "        train_pressure = paddle.stack(\n",
        "            x=[\n",
        "                paddle.to_tensor(\n",
        "                    data=self.load_pressure(data_dir, \"\", train_indices[0]), dtype=\"float32\"\n",
        "                )\n",
        "            ]\n",
        "        )\n",
        "\n",
        "        self.pressure_normalization = UnitGaussianNormalizer(\n",
        "            train_pressure, eps=1e-06, reduce_dim=(0), verbose=False\n",
        "        )\n",
        "\n",
        "        if n_train != 500:\n",
        "            mean, std = self.load_bound(\n",
        "                data_dir, filename=\"train_pressure_mean_std.txt\", eps=0.0\n",
        "            )\n",
        "            self.pressure_normalization.mean, self.pressure_normalization.std = mean[0], std[0]\n",
        "\n",
        "        self.min_bounds = paddle.to_tensor(data=min_bounds)\n",
        "        self.max_bounds = paddle.to_tensor(data=max_bounds)\n",
        "        normalized_query_points = self.location_normalization(\n",
        "            paddle.to_tensor(data=query_points), self.min_bounds, self.max_bounds\n",
        "        ).transpose(perm=[3, 0, 1, 2])\n",
        "        if closest_points_to_query:\n",
        "            train_closest_points = self.location_normalization(\n",
        "                train_closest_points, self.min_bounds, self.max_bounds\n",
        "            ).transpose(perm=[0, 4, 1, 2, 3])\n",
        "            test_closest_points = self.location_normalization(\n",
        "                test_closest_points, self.min_bounds, self.max_bounds\n",
        "            ).transpose(perm=[0, 4, 1, 2, 3])\n",
        "        location_norm_fn = lambda x: self.location_normalization(\n",
        "            x, self.min_bounds, self.max_bounds\n",
        "        )\n",
        "        info_norm_fn = lambda x: self.info_normalization(\n",
        "            x, min_info_bounds, max_info_bounds\n",
        "        )\n",
        "        area_norm_fn = lambda x: self.area_normalization(\n",
        "            x, min_area_bound[0], max_area_bound[0]\n",
        "        )\n",
        "\n",
        "        self._train_data = DictDatasetWithConstant(\n",
        "            {\"sdf\": train_sdf},\n",
        "            {\"sdf_query_points\": normalized_query_points, \"data_dir\": data_dir, \"data_module\": self,\n",
        "             \"mesh_index\": train_indices},\n",
        "        )\n",
        "\n",
        "        self._test_data = DictDatasetWithConstant(\n",
        "            {\"sdf\": test_sdf},\n",
        "            {\"sdf_query_points\": normalized_query_points, \"data_dir\": test_data_dir, \"data_module\": self,\n",
        "             \"mesh_index\": test_indices},\n",
        "        )\n",
        "\n",
        "        if closest_points_to_query:\n",
        "            # self._train_data.data_dict[\"closest_points\"] = train_closest_points\n",
        "            self._test_data.data_dict[\"closest_points\"] = test_closest_points\n",
        "        self._aggregatable = ['sdf', 'closest_points', 'sdf_query_points']\n",
        "        self.output_normalization = [self.pressure_normalization]\n",
        "\n",
        "    def get_mesh_path(self, data_dir: Path, subfolder: str, mesh_ind: int) -> Path:\n",
        "        return data_dir / subfolder / (\"mesh_\" + str(mesh_ind).zfill(4) + \".ply\")\n",
        "\n",
        "    def get_pressure_data_path(\n",
        "            self, data_dir: Path, subfolder: str, mesh_ind: int\n",
        "    ) -> Path:\n",
        "        return data_dir / subfolder / (\"press_\" + str(mesh_ind).zfill(4) + \".npy\")\n",
        "\n",
        "    def get_wss_data_path(\n",
        "            self, data_dir: Path, subfolder: str, mesh_ind: int\n",
        "    ) -> Path:\n",
        "        return data_dir / subfolder / (\"wallshearstress_\" + str(mesh_ind).zfill(4) + \".npy\")\n",
        "\n",
        "    def load_wss(\n",
        "            self, data_dir: Path, subfolder: str, mesh_index: int\n",
        "    ) -> np.ndarray:\n",
        "        wss_path = self.get_wss_data_path(data_dir, subfolder, mesh_index)\n",
        "        assert wss_path.exists(), \"wallshearstress data does not exist\"\n",
        "        wss = np.load(wss_path).astype(np.float32)\n",
        "        return wss\n",
        "\n",
        "    def load_pressure(\n",
        "            self, data_dir: Path, subfolder: str, mesh_index: int\n",
        "    ) -> np.ndarray:\n",
        "        press_path = self.get_pressure_data_path(data_dir, subfolder, mesh_index)\n",
        "        print(press_path)\n",
        "        assert press_path.exists(), \"Pressure data does not exist\"\n",
        "        press = np.load(press_path).reshape((-1,)).astype(np.float32)\n",
        "        return press\n",
        "\n",
        "    def load_centroid(\n",
        "            self, data_dir: Path, subfolder: str, mesh_index: int\n",
        "    ) -> np.ndarray:\n",
        "        centroid_path = data_dir / subfolder / (\"centroid_\" + str(mesh_index).zfill(4) + \".npy\")\n",
        "        assert centroid_path.exists(), \"Centroid data does not exist\"\n",
        "        centroid = np.load(centroid_path).reshape((1, -1, 3)).astype(np.float32)\n",
        "        return centroid\n",
        "\n",
        "    def compute_df(\n",
        "            self, mesh: Union[Path, o3d.t.geometry.TriangleMesh], query_points\n",
        "    ) -> np.ndarray:\n",
        "        if isinstance(mesh, Path):\n",
        "            mesh = self.load_mesh(mesh)\n",
        "        scene = o3d.t.geometry.RaycastingScene()\n",
        "        _ = scene.add_triangles(mesh)\n",
        "        distance = scene.compute_distance(query_points).numpy()\n",
        "        return distance\n",
        "\n",
        "    def df_from_mesh(\n",
        "            self, mesh_path: Path, query_points: np.ndarray, closest_points: bool\n",
        "    ) -> Tuple[np.ndarray, np.ndarray]:\n",
        "        mesh = self.mesh_to_tmesh(self.load_mesh(mesh_path))\n",
        "        df = self.compute_df(mesh, query_points)\n",
        "\n",
        "        if closest_points:\n",
        "            closest_points = self.closest_points_to_query_from_mesh(mesh, query_points)\n",
        "        else:\n",
        "            closest_points = None\n",
        "        return df, closest_points\n",
        "\n",
        "    def info_normalization(\n",
        "            self, info: dict, min_bounds: List[float], max_bounds: List[float]\n",
        "    ) -> dict:\n",
        "        \"\"\"\n",
        "        Normalize info to [0, 1].\n",
        "        \"\"\"\n",
        "        for i, (k, v) in enumerate(info.items()):\n",
        "            info[k] = (v - min_bounds[i]) / (max_bounds[i] - min_bounds[i])\n",
        "        return info\n",
        "\n",
        "    def area_normalization(\n",
        "            self, area: paddle.Tensor, min_bounds: float, max_bounds: float\n",
        "    ) -> paddle.Tensor:\n",
        "        \"\"\"\n",
        "        Normalize info to [0, 1].\n",
        "        \"\"\"\n",
        "        return (area - min_bounds) / (max_bounds - min_bounds)\n",
        "\n",
        "    def wss_normalization(\n",
        "            self,\n",
        "            area: paddle.Tensor,\n",
        "            min_bounds,\n",
        "            max_bounds,\n",
        "    ) -> paddle.Tensor:\n",
        "        \"\"\"\n",
        "        Normalize info to [0, 1].\n",
        "        \"\"\"\n",
        "        return (area - min_bounds) / (max_bounds - min_bounds)\n",
        "\n",
        "    def collate_fn(self, batch):\n",
        "        aggr_dict = {}\n",
        "        for key in self._aggregatable:\n",
        "            aggr_dict.update(\n",
        "                {key: paddle.stack(x=[data_dict[key] for data_dict in batch])}\n",
        "            )\n",
        "        remaining = list(set(batch[0].keys()) - set(self._aggregatable))\n",
        "        for key in remaining:\n",
        "            new_mini_batch_list = [data_dict[key] for data_dict in batch]\n",
        "            if len(new_mini_batch_list) == 1:\n",
        "                aggr_dict.update({key: new_mini_batch_list[0]})\n",
        "            else:\n",
        "                aggr_dict.update({key: new_mini_batch_list})\n",
        "\n",
        "                # TODO for competitor : because centroid is not the same length, so a padding strategy may be needed.\n",
        "                raise NotImplementedError(\n",
        "                    \"Not implemented for more than one element in the batch.\")\n",
        "        return aggr_dict\n",
        "\n",
        "\n",
        "class GCNDataModule(CFDDataModule):\n",
        "    def __init__(\n",
        "            self,\n",
        "            data_dir,\n",
        "            n_train: int = 10,\n",
        "            n_test: int = 5,\n",
        "            eps=0.01,\n",
        "            test_data_dir=None,\n",
        "    ):\n",
        "        BaseDataModule.__init__(self)\n",
        "\n",
        "        if isinstance(data_dir, str):\n",
        "            data_dir = Path(data_dir)\n",
        "            test_data_dir = Path(test_data_dir)\n",
        "\n",
        "        data_dir = data_dir.expanduser()\n",
        "        test_data_dir = test_data_dir.expanduser()\n",
        "        assert data_dir.exists(), \"Path does not exist\"\n",
        "        assert data_dir.is_dir(), \"Path is not a directory\"\n",
        "        self.data_dir = data_dir\n",
        "        self.test_data_dir = test_data_dir\n",
        "\n",
        "        min_bounds, max_bounds = self.load_bound(data_dir, eps=eps)\n",
        "        valid_mesh_inds_train = self.load_valid_mesh_indices(data_dir)\n",
        "        valid_mesh_inds_test = self.load_valid_mesh_indices(test_data_dir)\n",
        "        valid_mesh_inds = valid_mesh_inds_train + valid_mesh_inds_test\n",
        "        assert n_train + n_test <= len(valid_mesh_inds), \"Not enough data\"\n",
        "        if n_train + n_test < len(valid_mesh_inds):\n",
        "            warnings.warn(\n",
        "                f\"{len(valid_mesh_inds)} meshes are available, but {n_train + n_test} are requested.\"\n",
        "            )\n",
        "\n",
        "        train_indices = valid_mesh_inds_train[:n_train]\n",
        "        test_indices = valid_mesh_inds_test[:n_test]\n",
        "        train_mesh_paths = [self.get_mesh_path(data_dir, i) for i in train_indices]\n",
        "        test_mesh_paths = [self.get_mesh_path(test_data_dir, i) for i in test_indices]\n",
        "        self.test_mesh_paths = test_mesh_paths\n",
        "        self.test_indices = test_indices\n",
        "        self.train_mesh_paths = train_mesh_paths\n",
        "\n",
        "        train_sdf_mesh_vertices = [\n",
        "            self.vertices_and_edges_from_mesh(\n",
        "                mesh_path,\n",
        "            )\n",
        "            for mesh_path in train_mesh_paths\n",
        "        ]\n",
        "\n",
        "        train_vertices = np.stack([vertices for vertices, _ in train_sdf_mesh_vertices], axis=0)\n",
        "        train_edges = np.stack([edges for _, edges in train_sdf_mesh_vertices], axis=0)\n",
        "\n",
        "        del train_sdf_mesh_vertices\n",
        "        train_pressure = np.stack([self.load_pressure(data_dir, mesh_index) for mesh_index in train_indices])\n",
        "\n",
        "        # train_vertices = self.location_normalization(\n",
        "        #     train_vertices, np.asarray(min_bounds), np.asarray(max_bounds)\n",
        "        # )\n",
        "\n",
        "        test_mesh_vertices_edges = [\n",
        "            self.vertices_and_edges_from_mesh(\n",
        "                mesh_path,\n",
        "            )\n",
        "            for mesh_path in test_mesh_paths\n",
        "        ]\n",
        "        test_vertices = np.stack([vertices for vertices, _ in test_mesh_vertices_edges], axis=0)\n",
        "        test_edges = np.stack([edges for _, edges in test_mesh_vertices_edges], axis=0)\n",
        "\n",
        "        del test_mesh_vertices_edges\n",
        "\n",
        "        # fake test pressure\n",
        "        if (test_data_dir.name == 'data_test_A') or (test_data_dir.name == 'data_test_B'):\n",
        "            print(f\"fake pressure for {test_data_dir.name}\")\n",
        "            for v, mesh_index in zip(test_vertices, test_indices):\n",
        "                p_length = v.shape[0] + 96  # 96 = 112 - 16 for pressure truncation\n",
        "                p_test_fake = np.ones((p_length,), dtype=np.float32)\n",
        "                np.save(self.get_pressure_data_path(test_data_dir, mesh_index), p_test_fake)\n",
        "        test_pressure = np.stack([self.load_pressure(test_data_dir, mesh_index) for mesh_index in test_indices])\n",
        "\n",
        "        # test_vertices = self.location_normalization(\n",
        "        #   test_vertices, np.asarray(min_bounds), np.asarray(max_bounds)\n",
        "        #  )\n",
        "\n",
        "        self._train_data = VerticeEdgeDataset(train_vertices, train_edges, train_pressure)\n",
        "        self._test_data = VerticeEdgeDataset(test_vertices, test_edges, test_pressure)\n",
        "\n",
        "        self._aggregatable = ['vertices', 'pressure', 'edges']\n",
        "\n",
        "    def load_bound(\n",
        "            self, data_dir, filename=\"watertight_global_bounds.txt\", eps=1e-06\n",
        "    ) -> Tuple[List[float], List[float]]:\n",
        "        with open(data_dir / filename, \"r\") as fp:\n",
        "            min_bounds = fp.readline().split(\" \")\n",
        "            max_bounds = fp.readline().split(\" \")\n",
        "            min_bounds = [(float(a) - eps) for a in min_bounds]\n",
        "            max_bounds = [(float(a) + eps) for a in max_bounds]\n",
        "        return min_bounds, max_bounds\n",
        "\n",
        "    def location_normalization(\n",
        "            self,\n",
        "            locations: np.ndarray,\n",
        "            min_bounds: np.ndarray,\n",
        "            max_bounds: np.ndarray,\n",
        "    ):\n",
        "        \"\"\"\n",
        "        Normalize locations to [-1, 1].\n",
        "        \"\"\"\n",
        "        locations = (locations - min_bounds) / (max_bounds - min_bounds)\n",
        "        locations = 2 * locations - 1\n",
        "        return locations\n",
        "\n",
        "    def vertices_and_edges_from_mesh(\n",
        "            self, mesh_path: Path,\n",
        "    ) -> Tuple[np.ndarray, np.ndarray]:\n",
        "        mesh = self.load_mesh(mesh_path)\n",
        "        vertices = np.asarray(mesh.vertices, dtype=np.float32)\n",
        "        edge_index = self.create_graph_from_mesh(mesh)\n",
        "        return vertices, edge_index\n",
        "\n",
        "    def create_graph_from_mesh(self, mesh: Union[Path, o3d.t.geometry.TriangleMesh]):\n",
        "        edges = []\n",
        "        for triangle in np.asarray(mesh.triangles):\n",
        "            edges.extend([(triangle[0], triangle[1]),\n",
        "                          (triangle[1], triangle[2]),\n",
        "                          (triangle[2], triangle[0])])\n",
        "\n",
        "        edges = list(set(edges))\n",
        "        edge_index = np.array(edges, dtype=np.int64).T\n",
        "        return edge_index\n",
        "\n",
        "    # def collate_fn(self, batch):\n",
        "    #     vertices_batch = [item[0] for item in batch]\n",
        "    #     edges_batch = [item[1] for item in batch]\n",
        "    #     pressure_batch = [item[2] for item in batch]\n",
        "    #     return {\n",
        "    #         'vertices': paddle.to_tensor(vertices_batch),\n",
        "    #         'edges': paddle.to_tensor(edges_batch),\n",
        "    #         'pressure': paddle.to_tensor(pressure_batch)\n",
        "    #     }\n",
        "\n",
        "\n",
        "class TrackADataset(paddle.io.Dataset):\n",
        "\n",
        "    def __init__(self, train_data_dir, test_data_dir, test=False):\n",
        "        super().__init__()\n",
        "        self.train_data_dir = train_data_dir\n",
        "        self.test_data_dir = test_data_dir\n",
        "\n",
        "        if test:\n",
        "            self.data_dir = test_data_dir\n",
        "        else:\n",
        "            self.data_dir = train_data_dir\n",
        "\n",
        "        valid_mesh_inds = self.load_valid_mesh_indices(self.data_dir)\n",
        "\n",
        "        train_indices = valid_mesh_inds[:500]\n",
        "        test_indices = valid_mesh_inds[-50:]\n",
        "\n",
        "        self.indices = train_indices if not test else test_indices\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.indices)\n",
        "\n",
        "    def get_mesh_path(self, data_dir: Path, mesh_ind: int) -> Path:\n",
        "        return os.path.join(data_dir, \"mesh_\" + str(mesh_ind).zfill(3) + \".ply\")\n",
        "\n",
        "    def load_node_edges(self, data_dir, mesh_index):\n",
        "        mesh_path = self.get_mesh_path(data_dir, mesh_index)\n",
        "        mesh = o3d.io.read_triangle_mesh(str(mesh_path))\n",
        "        node_features, edge_index = self.create_graph_from_mesh(mesh)\n",
        "        return node_features, edge_index\n",
        "\n",
        "    def get_pressure_data_path(self, data_dir: Path, mesh_ind: int) -> Path:\n",
        "        return os.path.join(data_dir, \"press_\" + str(mesh_ind).zfill(3) + \".npy\")\n",
        "\n",
        "    def load_pressure(self, data_dir: Path, mesh_index: int) -> np.ndarray:\n",
        "        press_path = self.get_pressure_data_path(data_dir, mesh_index)\n",
        "        # assert press_path.exists(), \"Pressure data does not exist\"\n",
        "        press = np.load(press_path).reshape((-1,)).astype(np.float32)\n",
        "        press = np.concatenate((press[0:16], press[112:]), axis=0)\n",
        "        return press\n",
        "\n",
        "    def load_valid_mesh_indices(\n",
        "            self, data_dir, filename=\"watertight_meshes.txt\"\n",
        "    ) -> List[int]:\n",
        "        with open(os.path.join(data_dir, filename), \"r\") as fp:\n",
        "            mesh_ind = fp.read().split(\"\\n\")\n",
        "            mesh_ind = [int(a) for a in mesh_ind]\n",
        "        return mesh_ind\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        index_ = self.indices[index]\n",
        "        node_features, edge_features = self.load_node_edges(self.data_dir, index_)\n",
        "        press = self.load_pressure(self.data_dir, index_)\n",
        "        return node_features, edge_features, press\n",
        "\n",
        "    def create_graph_from_mesh(self, mesh):\n",
        "        vertices = np.asarray(mesh.vertices, dtype=np.float32)\n",
        "\n",
        "        edges = []\n",
        "        for triangle in np.asarray(mesh.triangles):\n",
        "            edges.extend([(triangle[0], triangle[1]),\n",
        "                          (triangle[1], triangle[2]),\n",
        "                          (triangle[2], triangle[0])])\n",
        "\n",
        "        edges = list(set(edges))\n",
        "        edge_index = np.array(edges, dtype=np.int64).T\n",
        "\n",
        "        return paddle.to_tensor(vertices), paddle.to_tensor(edge_index)\n",
        "\n",
        "\n",
        "def instantiate_datamodule(config):\n",
        "    if config.data_module == \"CFDSDFDataModule\":\n",
        "        assert config.sdf_spatial_resolution is not None\n",
        "        return CFDSDFDataModule(\n",
        "            config.data_dir,\n",
        "            config.n_train,\n",
        "            config.n_test,\n",
        "            spatial_resolution=config.sdf_spatial_resolution,\n",
        "            test_data_dir=config.test_data_dir,\n",
        "            use_aug_feature=config.use_aug_feature,\n",
        "            rag_data_dir=config.rag_data_dir,\n",
        "        )\n",
        "\n",
        "    if config.data_module == \"CarDataModule\":\n",
        "        assert config.sdf_spatial_resolution is not None\n",
        "        return CarDataModule(\n",
        "            config.data_dir,\n",
        "            config.test_data_dir,\n",
        "            config.n_train,\n",
        "            config.n_test,\n",
        "            spatial_resolution=config.sdf_spatial_resolution,\n",
        "\n",
        "\n",
        "        )\n",
        "\n",
        "    if config.data_module == \"GCNDataModule\":\n",
        "        return GCNDataModule(\n",
        "            config.data_dir,\n",
        "            config.n_train,\n",
        "            config.n_test,\n",
        "            test_data_dir=config.test_data_dir,\n",
        "        )\n",
        "    else:\n",
        "        raise NotImplementedError(f\"Unknown datamodule: {config.data_module}\")"
      ],
      "metadata": {
        "id": "GwjmcQXdovAW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class StepDecay(LRScheduler):\n",
        "    def __init__(self, learning_rate, step_size, gamma=0.1, last_epoch=-1, verbose=False):\n",
        "        if not isinstance(step_size, int):\n",
        "            raise TypeError(\"The type of 'step_size' must be 'int', but received %s.\" % type(step_size))\n",
        "        if gamma >= 1.0:\n",
        "            raise ValueError(\"gamma should be < 1.0.\")\n",
        "\n",
        "        self.step_size = step_size\n",
        "        self.gamma = gamma\n",
        "        super().__init__(learning_rate, last_epoch, verbose)\n",
        "\n",
        "    def get_lr(self):\n",
        "        i = self.last_epoch // self.step_size\n",
        "        return self.base_lr * (self.gamma ** i)\n",
        "\n",
        "\n",
        "def instantiate_scheduler(config):\n",
        "    if config.opt_scheduler == \"CosineAnnealingLR\":\n",
        "        scheduler = paddle.optimizer.lr.CosineAnnealingDecay(config.lr, T_max=config.opt_scheduler_T_max)\n",
        "    elif config.opt_scheduler == \"StepLR\":\n",
        "        scheduler = StepDecay(config.lr, step_size=config.opt_step_size, gamma=config.opt_gamma)\n",
        "    else:\n",
        "        raise ValueError(f\"Got {config.opt_scheduler=}\")\n",
        "    return scheduler\n",
        "\n",
        "\n",
        "class LpLoss(object):\n",
        "    def __init__(self, d=2, p=2, size_average=True, reduction=True):\n",
        "        super(LpLoss, self).__init__()\n",
        "        assert d > 0 and p > 0\n",
        "\n",
        "        self.d = d\n",
        "        self.p = p\n",
        "        self.reduction = reduction\n",
        "        self.size_average = size_average\n",
        "\n",
        "    def abs(self, x, y):\n",
        "        num_examples = x.size()[0]\n",
        "        h = 1.0 / (x.size()[1] - 1.0)\n",
        "        all_norms = (h ** (self.d / self.p)) * paddle.norm(\n",
        "            x.reshape((num_examples, -1)) - y.reshape((num_examples, -1)), self.p, 1\n",
        "        )\n",
        "        if self.reduction:\n",
        "            if self.size_average:\n",
        "                return paddle.mean(all_norms)\n",
        "            else:\n",
        "                return paddle.sum(all_norms)\n",
        "        return all_norms\n",
        "\n",
        "    def rel(self, x, y):\n",
        "        diff_norms = paddle.norm(x - y, 2)\n",
        "        y_norms = paddle.norm(y, self.p)\n",
        "        if self.reduction:\n",
        "            if self.size_average:\n",
        "                return paddle.mean(diff_norms / y_norms)\n",
        "            else:\n",
        "                return paddle.sum(diff_norms / y_norms)\n",
        "        return diff_norms / y_norms\n",
        "\n",
        "    def __call__(self, x, y):\n",
        "        return self.rel(x, y)\n",
        "\n",
        "\n",
        "def set_seed(seed: int = 0):\n",
        "    paddle.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    import random\n",
        "    random.seed(seed)\n",
        "\n",
        "\n",
        "def str2intlist(s: str) -> List[int]:\n",
        "    return [int(item.strip()) for item in s.split(\",\")]\n",
        "\n",
        "\n",
        "def parse_args(yaml=\"UnetShapeNetCar.yaml\"):\n",
        "    parser = argparse.ArgumentParser()\n",
        "    parser.add_argument(\"--config\", type=str, default=\"configs/\" + yaml, help=\"Path to the configuration file\")\n",
        "    parser.add_argument(\"--device\", type=str, default=\"cuda\", help=\"Device to use for training (cuda or cpu)\")\n",
        "    parser.add_argument(\"--lr\", type=float, default=None, help=\"Learning rate\")\n",
        "    parser.add_argument(\"--batch_size\", type=int, default=None, help=\"Batch size\")\n",
        "    parser.add_argument(\"--num_epochs\", type=int, default=None, help=\"Number of epochs\")\n",
        "    parser.add_argument(\"--checkpoint\", type=str, default=\"\", help=\"Path to the checkpoint file to resume training\")\n",
        "    parser.add_argument(\"--output\", type=str, default=\"./output\", help=\"Path to the output directory\")\n",
        "    parser.add_argument(\"--log\", type=str, default=\"log\", help=\"Path to the log directory\")\n",
        "    parser.add_argument(\"--logger_types\", type=str, nargs=\"+\", default=\"\")\n",
        "    parser.add_argument(\"--seed\", type=int, default=0, help=\"Random seed for training\")\n",
        "    parser.add_argument(\"--model\", type=str, default=None, help=\"Model name\")\n",
        "    parser.add_argument(\"--sdf_spatial_resolution\", type=str2intlist, default=None,\n",
        "                        help=\"SDF spatial resolution. Use comma to separate the values e.g. 32,32,32.\")\n",
        "\n",
        "    args, _ = parser.parse_known_args()\n",
        "    return args\n",
        "\n",
        "\n",
        "def load_config(config_path):\n",
        "    def include_constructor(loader, node):\n",
        "        current_file_path = loader.name\n",
        "        base_folder = os.path.dirname(current_file_path)\n",
        "        included_file = os.path.join(base_folder, loader.construct_scalar(node))\n",
        "        with open(included_file, \"r\") as file:\n",
        "            return yaml.load(file, Loader=yaml.Loader)\n",
        "\n",
        "    yaml.Loader.add_constructor(\"!include\", include_constructor)\n",
        "\n",
        "    with open(config_path, \"r\") as f:\n",
        "        config = yaml.load(f, Loader=yaml.Loader)\n",
        "\n",
        "    config_flat = flatten_dict(config)\n",
        "    config_flat = DotDict(config_flat)\n",
        "\n",
        "    return config_flat\n",
        "\n",
        "\n",
        "def extract_numbers(s):\n",
        "    return [int(digit) for digit in re.findall(r'\\d+', s)]\n",
        "\n",
        "\n",
        "def write_to_vtk(out_dict, point_data_pos=\"press on mesh points\", mesh_path=None, track=None):\n",
        "    p = out_dict[\"denormalized_pressure\"].reshape((-1,)).astype(np.float64)\n",
        "    index = extract_numbers(mesh_path.name)[0]\n",
        "\n",
        "    if track == \"Track_A\":\n",
        "        index = str(index).zfill(3)\n",
        "    elif track == \"Track_B\":\n",
        "        index = str(index).zfill(4)\n",
        "\n",
        "    # print(f\"Pressure shape for mesh {index} = {p.shape}\")\n",
        "    p = p.numpy()\n",
        "\n",
        "    if point_data_pos == \"press on mesh points\":\n",
        "        mesh = meshio.read(mesh_path)\n",
        "        mesh.point_data[\"p\"] = p\n",
        "        if \"pred wss_x\" in out_dict:\n",
        "            wss_x = out_dict[\"pred wss_x\"]\n",
        "            mesh.point_data[\"wss_x\"] = wss_x.numpy()\n",
        "    elif point_data_pos == \"press on mesh cells\":\n",
        "        points = np.load(mesh_path.parent / f\"centroid_{index}.npy\")\n",
        "        npoint = points.shape[0]\n",
        "        mesh = meshio.Mesh(points=points, cells=[(\"vertex\", np.arange(npoint).reshape(npoint, 1))])\n",
        "        mesh.point_data = {\"p\": p}\n",
        "\n",
        "    print(f\"write : ./output/{mesh_path.parent.name}_{index}.vtk\")\n",
        "    mesh.write(f\"./output/{mesh_path.parent.name}_{index}.vtk\")\n",
        "\n",
        "\n",
        "@paddle.no_grad()\n",
        "def eval(model, datamodule, config, loss_fn=None, track=\"Track_A\"):\n",
        "    test_loader = datamodule.test_dataloader(batch_size=config.eval_batch_size, shuffle=False)\n",
        "    data_list = []\n",
        "\n",
        "    os.makedirs(\"./output/\", exist_ok=True)\n",
        "\n",
        "    for i, data_dict in enumerate(test_loader):\n",
        "        out_dict = model.eval_dict(data_dict, loss_fn=loss_fn, decode_fn= datamodule.output_normalization[0].decode)\n",
        "\n",
        "        if 'l2 decoded pressure' in out_dict:\n",
        "            if i == 0:\n",
        "                data_list.append(['id', 'l2 p decoded'])\n",
        "            data_list.append([i, float(out_dict['l2_decoded'])])\n",
        "        if 'l2_decoded' in out_dict:\n",
        "            if i == 0:\n",
        "                data_list.append(['id', 'l2 p decoded'])\n",
        "            data_list.append([i, float(out_dict['l2_decoded'])])\n",
        "\n",
        "        if config.write_to_vtk is True:\n",
        "            # print(\"datamodule.test_mesh_paths = \", datamodule.test_mesh_paths[i])\n",
        "            write_to_vtk(out_dict, config.point_data_pos, datamodule.test_mesh_paths[i], track)\n",
        "\n",
        "        p = out_dict[\"denormalized_pressure\"].reshape((-1,)).astype(np.float64)\n",
        "\n",
        "        if track == \"Track_A\":\n",
        "            n = 3\n",
        "        elif track == \"Track_B\":\n",
        "            n = 0\n",
        "        test_indice = datamodule.test_indices[i]\n",
        "        npy_leaderboard = f\"./result/press_{str(test_indice).zfill(n)}.npy\"\n",
        "        if not os.path.exists(f\"./result/{track}\"):\n",
        "            os.makedirs(f\"./result/\", exist_ok=True)\n",
        "        print(f\"saving *.npy file for [{track}] leaderboard : \", npy_leaderboard)\n",
        "        np.save(npy_leaderboard, p)\n",
        "\n",
        "    return\n",
        "\n"
      ],
      "metadata": {
        "id": "do5ddPrioxKa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class FNO1d1(nn.Layer):\n",
        "    def __init__(self, input_size, modes, width):\n",
        "        super(FNO1d1, self).__init__()\n",
        "\n",
        "        self.modes1 = modes\n",
        "        self.width = width\n",
        "        self.fc0 = nn.Linear(input_size, self.width)  # 3 for coordinates, 1 for GCN output\n",
        "        self.conv0 = SpectralConv1d(self.width, self.width, self.modes1)\n",
        "        self.conv1 = SpectralConv1d(self.width, self.width, self.modes1)\n",
        "        self.conv2 = SpectralConv1d(self.width, self.width, self.modes1)\n",
        "        self.conv3 = SpectralConv1d(self.width, self.width, self.modes1)\n",
        "        self.w0 = nn.Conv1D(self.width, self.width, 1)\n",
        "        self.w1 = nn.Conv1D(self.width, self.width, 1)\n",
        "        self.w2 = nn.Conv1D(self.width, self.width, 1)\n",
        "        self.w3 = nn.Conv1D(self.width, self.width, 1)\n",
        "        self.fc1 = nn.Linear(self.width, 128)\n",
        "        self.fc2 = nn.Linear(128, 8)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.fc0(x)\n",
        "        x = x.transpose((0, 2, 1))\n",
        "\n",
        "        x1 = self.conv0(x)\n",
        "        x2 = self.w0(x)\n",
        "        x = x1 + x2\n",
        "        x = F.gelu(x)\n",
        "\n",
        "        x1 = self.conv1(x)\n",
        "        x2 = self.w1(x)\n",
        "        x = x1 + x2\n",
        "        x = F.gelu(x)\n",
        "\n",
        "        x1 = self.conv2(x)\n",
        "        x2 = self.w2(x)\n",
        "        x = x1 + x2\n",
        "        x = F.gelu(x)\n",
        "\n",
        "        x1 = self.conv3(x)\n",
        "        x2 = self.w3(x)\n",
        "        x = x1 + x2\n",
        "\n",
        "        x = x.transpose((0, 2, 1))\n",
        "        x = self.fc1(x)\n",
        "        x = F.gelu(x)\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class GCN_FNO(nn.Layer):\n",
        "    def __init__(self, gcn_input_size, gcn_hidden_size, gcn_output_size, fno_modes, fno_width):\n",
        "        super(GCN_FNO, self).__init__()\n",
        "        self.gcn = GCN(gcn_input_size, gcn_hidden_size, gcn_output_size)\n",
        "        self.fno = FNO1d1(12, fno_modes, fno_width)\n",
        "\n",
        "    def forward(self, x, edge_index, use_hidden_states=False, loss_dict=False):\n",
        "        gcn_output = self.gcn(x, edge_index)\n",
        "        fno_input = paddle.concat([x, gcn_output], axis=-1)\n",
        "        output = self.fno(fno_input)\n",
        "        hidden_states = None\n",
        "        return output, hidden_states\n",
        "\n",
        "    @paddle.no_grad()\n",
        "    def eval_dict(self, data_dict, loss_fn=None, decode_fn=None, **kwargs):\n",
        "        use_hidden_states = True\n",
        "        vertices, edges, pressure = data_dict\n",
        "        pred, hidden_states = self(vertices, edges, use_hidden_states=use_hidden_states)\n",
        "        out_dict = {'pressure': pred, 'hidden_states': hidden_states}\n",
        "        if loss_fn is not None:\n",
        "            gt = data_dict['pressure'].unsqueeze(-1)\n",
        "            out_dict['l2'] = loss_fn(pred, gt)\n",
        "            if decode_fn is not None:\n",
        "                pred_decoded = decode_fn(pred)\n",
        "                gt_decoded = decode_fn(gt)\n",
        "                out_dict['l2_decoded'] = loss_fn(pred_decoded, gt_decoded)\n",
        "        return out_dict\n",
        "\n",
        "    def loss_dict(self, data_dict, loss_fn=None, **kwargs):\n",
        "        vertices, edges, pressure = data_dict\n",
        "        pred, hidden_states = self(vertices, edges)\n",
        "        gt = pressure.unsqueeze(-1)\n",
        "        if loss_fn is None:\n",
        "            loss_fn = F.mse_loss\n",
        "        return loss_fn(pred, gt)\n",
        "\n",
        "class GCNLayer(nn.Layer):\n",
        "    def __init__(self, in_features, out_features):\n",
        "        super(GCNLayer, self).__init__()\n",
        "        self.linear = nn.Linear(in_features, out_features)\n",
        "\n",
        "    def forward(self, x, edge_index):\n",
        "        batch_size, num_nodes, _ = x.shape\n",
        "\n",
        "        out = paddle.zeros_like(x)\n",
        "        for b in range(batch_size):\n",
        "            row, col = edge_index[b]\n",
        "\n",
        "            deg = paddle.zeros((num_nodes,)) + 1e-6\n",
        "            ones = paddle.ones_like(row, dtype='float32')\n",
        "            deg = deg + paddle.scatter(\n",
        "                paddle.zeros_like(deg), row, ones, overwrite=False\n",
        "            )\n",
        "            deg_inv = deg.pow(-0.5)\n",
        "\n",
        "            edge_weight = deg_inv[row] * deg_inv[col]\n",
        "\n",
        "            for i in range(x.shape[-1]):\n",
        "                out[b, :, i] = paddle.scatter(\n",
        "                    out[b, :, i], row, x[b, col, i] * edge_weight, overwrite=False\n",
        "                )\n",
        "\n",
        "        return self.linear(out)\n",
        "\n",
        "\n",
        "class SelfAttention3D(nn.Layer):\n",
        "    def __init__(self, in_channels, heads=4):\n",
        "        super(SelfAttention3D, self).__init__()\n",
        "        self.heads = heads\n",
        "        self.in_channels = in_channels\n",
        "        self.scale = (in_channels // heads) ** -0.5\n",
        "\n",
        "        assert in_channels % heads == 0, f\"in_channels {in_channels} should be divisible by heads {heads}\"\n",
        "\n",
        "        self.qkv = nn.Conv3D(in_channels, in_channels * 3, kernel_size=1, bias_attr=False)\n",
        "        self.proj = nn.Conv3D(in_channels, in_channels, kernel_size=1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        b, c, h, w, d = x.shape\n",
        "        qkv = self.qkv(x)\n",
        "\n",
        "        # 重塑并转置 qkv\n",
        "        qkv = qkv.reshape((b, 3, self.heads, c // self.heads, h * w * d))\n",
        "        qkv = qkv.transpose((1, 0, 2, 4, 3))\n",
        "        q, k, v = qkv[0], qkv[1], qkv[2]\n",
        "\n",
        "        # 计算注意力\n",
        "        attn = paddle.matmul(q, k.transpose((0, 1, 3, 2))) * self.scale\n",
        "        attn = F.softmax(attn, axis=-1)\n",
        "\n",
        "        # 应用注意力\n",
        "        x = paddle.matmul(attn, v)\n",
        "\n",
        "        # 重塑回原始维度\n",
        "        x = x.transpose((0, 2, 1, 3)).reshape((b, c, h, w, d))\n",
        "        x = self.proj(x)\n",
        "        return x\n",
        "\n",
        "class SpectralConv3d(nn.Layer):\n",
        "    def __init__(self, in_channels, out_channels, modes1, modes2, modes3):\n",
        "        super(SpectralConv3d, self).__init__()\n",
        "        self.in_channels = in_channels\n",
        "        self.out_channels = out_channels\n",
        "        self.modes1 = modes1\n",
        "        self.modes2 = modes2\n",
        "        self.modes3 = modes3\n",
        "\n",
        "        self.scale = (1 / (in_channels * out_channels))\n",
        "        self.weights1 = paddle.create_parameter(\n",
        "            shape=[in_channels, out_channels, modes1, modes2, modes3],\n",
        "            dtype='float32',\n",
        "            default_initializer=nn.initializer.Uniform(-self.scale, self.scale))\n",
        "        self.weights2 = paddle.create_parameter(\n",
        "            shape=[in_channels, out_channels, modes1, modes2, modes3],\n",
        "            dtype='float32',\n",
        "            default_initializer=nn.initializer.Uniform(-self.scale, self.scale))\n",
        "        self.weights3 = paddle.create_parameter(\n",
        "            shape=[in_channels, out_channels, modes1, modes2, modes3],\n",
        "            dtype='float32',\n",
        "            default_initializer=nn.initializer.Uniform(-self.scale, self.scale))\n",
        "        self.weights4 = paddle.create_parameter(\n",
        "            shape=[in_channels, out_channels, modes1, modes2, modes3],\n",
        "            dtype='float32',\n",
        "            default_initializer=nn.initializer.Uniform(-self.scale, self.scale))\n",
        "\n",
        "    def compl_mul3d(self, input, weights):\n",
        "        return paddle.einsum(\"bixyz,ioxyz->boxyz\", input, weights)\n",
        "\n",
        "    def forward(self, x):\n",
        "        batchsize = x.shape[0]\n",
        "        x_ft = paddle.fft.rfftn(x, axes=(-3, -2, -1))\n",
        "\n",
        "        out_ft = paddle.zeros(\n",
        "            (batchsize, self.out_channels, x.shape[-3], x.shape[-2], x.shape[-1]//2 + 1),\n",
        "            dtype=paddle.complex64)\n",
        "\n",
        "        out_ft[:, :, :self.modes1, :self.modes2, :self.modes3] = \\\n",
        "            self.compl_mul3d(x_ft[:, :, :self.modes1, :self.modes2, :self.modes3],\n",
        "                             self.weights1)\n",
        "        out_ft[:, :, -self.modes1:, :self.modes2, :self.modes3] = \\\n",
        "            self.compl_mul3d(x_ft[:, :, -self.modes1:, :self.modes2, :self.modes3],\n",
        "                             self.weights2)\n",
        "        out_ft[:, :, :self.modes1, -self.modes2:, :self.modes3] = \\\n",
        "            self.compl_mul3d(x_ft[:, :, :self.modes1, -self.modes2:, :self.modes3],\n",
        "                             self.weights3)\n",
        "        out_ft[:, :, -self.modes1:, -self.modes2:, :self.modes3] = \\\n",
        "            self.compl_mul3d(x_ft[:, :, -self.modes1:, -self.modes2:, :self.modes3],\n",
        "                             self.weights4)\n",
        "\n",
        "        x = paddle.fft.irfftn(out_ft, s=(x.shape[-3], x.shape[-2], x.shape[-1]))\n",
        "        return x\n",
        "\n",
        "class FNO3d(nn.Layer):\n",
        "    def __init__(self, modes1, modes2, modes3, width, add_attn=False):\n",
        "        super(FNO3d, self).__init__()\n",
        "\n",
        "        self.modes1 = modes1\n",
        "        self.modes2 = modes2\n",
        "        self.modes3 = modes3\n",
        "        self.width = width\n",
        "        self.padding = 6  # pad the domain if input is non-periodic\n",
        "\n",
        "        self.p = nn.Linear(7, self.width)  # input channel is 7: (v, p1, p2, p3, x1, x2, x3)\n",
        "        self.conv0 = SpectralConv3d(self.width, self.width, self.modes1, self.modes2, self.modes3)\n",
        "        self.conv1 = SpectralConv3d(self.width, self.width, self.modes1, self.modes2, self.modes3)\n",
        "        self.conv2 = SpectralConv3d(self.width, self.width, self.modes1, self.modes2, self.modes3)\n",
        "        self.conv3 = SpectralConv3d(self.width, self.width, self.modes1, self.modes2, self.modes3)\n",
        "        self.w0 = nn.Conv3D(self.width, self.width, 1)\n",
        "        self.w1 = nn.Conv3D(self.width, self.width, 1)\n",
        "        self.w2 = nn.Conv3D(self.width, self.width, 1)\n",
        "        self.w3 = nn.Conv3D(self.width, self.width, 1)\n",
        "        self.bn0 = nn.BatchNorm3D(self.width)\n",
        "        self.bn1 = nn.BatchNorm3D(self.width)\n",
        "        self.bn2 = nn.BatchNorm3D(self.width)\n",
        "        self.bn3 = nn.BatchNorm3D(self.width)\n",
        "\n",
        "        if add_attn:\n",
        "            self.attn0 = SelfAttention3D(self.width)\n",
        "            self.attn1 = SelfAttention3D(self.width)\n",
        "            self.attn2 = SelfAttention3D(self.width)\n",
        "            self.attn3 = SelfAttention3D(self.width)\n",
        "        self.add_attn = add_attn\n",
        "\n",
        "\n",
        "        # self.q = nn.Linear(self.width, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # print(f\"Input shape: {x.shape}\")\n",
        "        grid = self.get_grid(x.shape)\n",
        "        # import pudb;pu.db;\n",
        "        # print(grid.shape)\n",
        "        # print(x.shape)\n",
        "        x = paddle.concat((x.transpose((0,2,3,4,1)), grid), axis=-1)\n",
        "        x = self.p(x)\n",
        "        x = x.transpose((0, 4, 1, 2, 3))\n",
        "\n",
        "        # print(f\"Shape after initial processing: {x.shape}\")\n",
        "        # print(x.shape)\n",
        "\n",
        "        x1 = self.conv0(x)\n",
        "        x2 = self.w0(x)\n",
        "        x = x1 + x2\n",
        "        x = F.gelu(x)\n",
        "        if self.add_attn:\n",
        "            print(f\"Shape before attn0: {x.shape}\")\n",
        "            x = self.attn0(x)  # 应用自注意力\n",
        "            print(f\"Shape after attn0: {x.shape}\")\n",
        "\n",
        "        x1 = self.conv1(x)\n",
        "        x2 = self.w1(x)\n",
        "        x = x1 + x2\n",
        "        x = F.gelu(x)\n",
        "        if self.add_attn:\n",
        "            x = self.attn1(x)  # 应用自注意力\n",
        "\n",
        "        x1 = self.conv2(x)\n",
        "        x2 = self.w2(x)\n",
        "        x = x1 + x2\n",
        "        x = F.gelu(x)\n",
        "        if self.add_attn:\n",
        "            x = self.attn2(x)  # 应用自注意力\n",
        "\n",
        "        x1 = self.conv3(x)\n",
        "        x2 = self.w3(x)\n",
        "        x = x1 + x2\n",
        "        if self.add_attn:\n",
        "            x = self.attn3(x)  # 应用自注意力\n",
        "\n",
        "        x = x.transpose((0, 2, 3, 4, 1))\n",
        "        # x = self.q(x)\n",
        "        return x\n",
        "\n",
        "    def get_grid(self, shape):\n",
        "        # import pudb;pu.db;\n",
        "        batchsize, size_x, size_y, size_z = shape[0], shape[2], shape[3], shape[4]\n",
        "        gridx = paddle.linspace(0, 1, size_x)\n",
        "        gridx = gridx.reshape((1, size_x, 1, 1, 1)).tile((batchsize, 1, size_y, size_z, 1))\n",
        "        gridy = paddle.linspace(0, 1, size_y)\n",
        "        gridy = gridy.reshape((1, 1, size_y, 1, 1)).tile((batchsize, size_x, 1, size_z, 1))\n",
        "        gridz = paddle.linspace(0, 1, size_z)\n",
        "        gridz = gridz.reshape((1, 1, 1, size_z, 1)).tile((batchsize, size_x, size_y, 1, 1))\n",
        "        return paddle.concat((gridx, gridy, gridz), axis=-1)\n",
        "class FNOWithSamplePoints(nn.Layer):\n",
        "    def __init__(self, in_channels: int, out_channels: int, modes: int, width: int, use_position_input: bool=True, add_attn:bool=True):\n",
        "        super(FNOWithSamplePoints, self).__init__()\n",
        "        self.fno = FNO3d(modes, modes, modes, width, add_attn=add_attn)\n",
        "        self.final_mlp = nn.Sequential(\n",
        "            nn.Linear(width, width),\n",
        "            nn.GELU(),\n",
        "            nn.Linear(width, out_channels)\n",
        "        )\n",
        "        self.use_position_input = use_position_input\n",
        "\n",
        "    def forward(self, x, output_points, use_hidden_states=False, loss_dict=False):\n",
        "        hidden_states = None\n",
        "        x = self.fno(x)\n",
        "        # print(x.shape)\n",
        "        output_points = output_points.unsqueeze(2).unsqueeze(2)\n",
        "        # print(output_points.shape)\n",
        "        x = x.transpose((0,4,1,2,3))\n",
        "        x = F.grid_sample(x, output_points, align_corners=False)\n",
        "        # print(x.shape)\n",
        "        x = x.squeeze(3).squeeze(3).transpose((0, 2, 1))\n",
        "        # print(x.shape)\n",
        "\n",
        "        x = self.final_mlp(x)\n",
        "        if use_hidden_states:\n",
        "            hidden_states = x\n",
        "        return x, hidden_states\n",
        "\n",
        "    def data_dict_to_input(self, data_dict):\n",
        "        input_grid_features = data_dict['sdf'].unsqueeze(1)\n",
        "        if self.use_position_input:\n",
        "            grid_points = data_dict['sdf_query_points']\n",
        "            input_grid_features = paddle.concat((input_grid_features, grid_points), axis=1)\n",
        "        output_points = data_dict['vertices']\n",
        "        return input_grid_features, output_points\n",
        "\n",
        "    @paddle.no_grad()\n",
        "    def eval_dict(self, data_dict, loss_fn=None, decode_fn=None, **kwargs):\n",
        "        use_hidden_states = True\n",
        "        input_grid_features, output_points = self.data_dict_to_input(data_dict)\n",
        "        pred, hidden_states = self(input_grid_features, output_points, use_hidden_states=use_hidden_states)\n",
        "        out_dict = {'pressure': pred, 'hidden_states': hidden_states}\n",
        "        if loss_fn is not None:\n",
        "            gt = data_dict['pressure'].unsqueeze(-1)\n",
        "            out_dict['l2'] = loss_fn(pred, gt)\n",
        "            if decode_fn is not None:\n",
        "                pred_decoded = decode_fn(pred)\n",
        "                gt_decoded = decode_fn(gt)\n",
        "                out_dict['l2_decoded'] = loss_fn(pred_decoded, gt_decoded)\n",
        "        return out_dict\n",
        "\n",
        "    def loss_dict(self, data_dict, loss_fn=None, **kwargs):\n",
        "        input_grid_features, output_points = self.data_dict_to_input(data_dict)\n",
        "        pred, hidden_states = self(input_grid_features, output_points)\n",
        "        gt = data_dict['pressure'].unsqueeze(-1)\n",
        "        if loss_fn is None:\n",
        "            loss_fn = F.mse_loss\n",
        "        return {'loss': loss_fn(pred, gt)}\n",
        "\n",
        "\n",
        "\n",
        "class GCN(nn.Layer):\n",
        "    def __init__(self, input_size, hidden_size, output_size):\n",
        "        super(GCN, self).__init__()\n",
        "        self.conv1 = GCNLayer(input_size, hidden_size)\n",
        "        self.conv2 = GCNLayer(hidden_size, hidden_size)\n",
        "        self.linear = nn.Linear(hidden_size, output_size)\n",
        "\n",
        "    def forward(self, x, edge_index):\n",
        "        x = F.relu(self.conv1(x, edge_index))\n",
        "        x = F.relu(self.conv2(x, edge_index)) + x\n",
        "        x = self.linear(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class SpectralConv1d(nn.Layer):\n",
        "    def __init__(self, in_channels, out_channels, modes1):\n",
        "        super(SpectralConv1d, self).__init__()\n",
        "        self.in_channels = in_channels\n",
        "        self.out_channels = out_channels\n",
        "        self.modes1 = modes1\n",
        "\n",
        "        self.scale = (1 / (in_channels * out_channels))\n",
        "        self.weights1 = paddle.create_parameter(\n",
        "            shape=[in_channels, out_channels, modes1],\n",
        "            dtype='float32',\n",
        "            default_initializer=nn.initializer.Uniform(low=-self.scale, high=self.scale)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        batchsize = x.shape[0]\n",
        "        x_ft = paddle.fft.rfft(x)\n",
        "        out_ft = paddle.zeros((batchsize, self.out_channels, x.shape[-1] // 2 + 1), dtype=paddle.complex64)\n",
        "        out_ft[:, :, :self.modes1] = paddle.einsum(\"bix,iox->box\", x_ft[:, :, :self.modes1],\n",
        "                                                   paddle.complex(self.weights1, paddle.zeros_like(self.weights1)))\n",
        "        x = paddle.fft.irfft(out_ft, n=x.shape[-1])\n",
        "        return x\n",
        "\n",
        "\n",
        "class FNO1d(nn.Layer):\n",
        "    def __init__(self, modes, width):\n",
        "        super(FNO1d, self).__init__()\n",
        "\n",
        "        self.modes1 = modes\n",
        "        self.width = width\n",
        "        self.fc0 = nn.Linear(4, self.width)  # 3 for coordinates, 1 for GCN output\n",
        "        self.conv0 = SpectralConv1d(self.width, self.width, self.modes1)\n",
        "        self.conv1 = SpectralConv1d(self.width, self.width, self.modes1)\n",
        "        self.conv2 = SpectralConv1d(self.width, self.width, self.modes1)\n",
        "        self.conv3 = SpectralConv1d(self.width, self.width, self.modes1)\n",
        "        self.w0 = nn.Conv1D(self.width, self.width, 1)\n",
        "        self.w1 = nn.Conv1D(self.width, self.width, 1)\n",
        "        self.w2 = nn.Conv1D(self.width, self.width, 1)\n",
        "        self.w3 = nn.Conv1D(self.width, self.width, 1)\n",
        "        self.fc1 = nn.Linear(self.width, 128)\n",
        "        self.fc2 = nn.Linear(128, 1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.fc0(x)\n",
        "        x = x.transpose((0, 2, 1))\n",
        "\n",
        "        x1 = self.conv0(x)\n",
        "        x2 = self.w0(x)\n",
        "        x = x1 + x2\n",
        "        x = F.gelu(x)\n",
        "\n",
        "        x1 = self.conv1(x)\n",
        "        x2 = self.w1(x)\n",
        "        x = x1 + x2\n",
        "        x = F.gelu(x)\n",
        "\n",
        "        x1 = self.conv2(x)\n",
        "        x2 = self.w2(x)\n",
        "        x = x1 + x2\n",
        "        x = F.gelu(x)\n",
        "\n",
        "        x1 = self.conv3(x)\n",
        "        x2 = self.w3(x)\n",
        "        x = x1 + x2\n",
        "\n",
        "        x = x.transpose((0, 2, 1))\n",
        "        x = self.fc1(x)\n",
        "        x = F.gelu(x)\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class SDFWithGCNFNO(nn.Layer):\n",
        "    def __init__(self, gcn_input_size, gcn_hidden_size, gcn_output_size, gcnfno_modes, gcnfno_width,  fno_input_size,\n",
        "                 fno_output_size, final_output_size,  fno_modes, fno_width, add_attn, use_position_input):\n",
        "        super(SDFWithGCNFNO, self).__init__()\n",
        "        self.use_position_input = use_position_input\n",
        "        self.gcnfno = GCN_FNO(gcn_input_size, gcn_hidden_size, gcn_output_size, gcnfno_modes, gcnfno_width)\n",
        "        self.fno = FNOWithSamplePoints(\n",
        "            in_channels=fno_input_size,\n",
        "            out_channels=fno_output_size,\n",
        "            modes=fno_modes,  # 新增配置项\n",
        "            width=fno_width,  # 新增配置项\n",
        "            use_position_input=use_position_input,\n",
        "            add_attn=add_attn\n",
        "        )\n",
        "        # self.mlp = nn.Sequential(\n",
        "        #     nn.Linear(gcn_output_size + fno_output_size, gcn_hidden_size),\n",
        "        #     nn.Dropout(0.2),\n",
        "        #     nn.Linear(gcn_hidden_size, final_output_size),\n",
        "        # )\n",
        "        self.mlp = nn.Linear(gcn_output_size + fno_output_size, final_output_size)\n",
        "        #self.gcn = GCN(2, hidden_size=gcn_hidden_size, output_size=final_output_size)\n",
        "\n",
        "    def forward(self, vertices, edge_index, input_grid_features, use_hidden_states=False, loss_dict=False,\n",
        "                aug_features=None,  rag_pressure=None):\n",
        "        \"\"\"\n",
        "        x: vertices\n",
        "        edge_index: edges of vertices\n",
        "        input_grid_features: sdf\n",
        "        aug_features: additional features, with shape [batch, num_points, num_features]\n",
        "        rag_pressure: pressure of ragd with shape [batch, num_points]\n",
        "        \"\"\"\n",
        "        output_points = vertices\n",
        "        x_gcnfno, gcnfno_hidden = self.gcnfno(vertices, edge_index)\n",
        "\n",
        "        x_fno, fno_hidden = self.fno(input_grid_features, output_points[:, :, :3])\n",
        "\n",
        "        output = self.mlp(paddle.concat([x_gcnfno, x_fno], axis=-1))\n",
        "        output = output\n",
        "\n",
        "        return output, None\n",
        "\n",
        "    def loss_dict(self, data_dict, loss_fn=None, **kwargs):\n",
        "        vertices, edges, pressure = data_dict\n",
        "        pred, hidden_states = self(vertices, edges)\n",
        "        gt = pressure.unsqueeze(-1)\n",
        "        if loss_fn is None:\n",
        "            loss_fn = F.mse_loss\n",
        "        return loss_fn(pred, gt)\n",
        "\n",
        "    def data_dict_to_input(self, data_dict):\n",
        "        input_grid_features = data_dict['sdf'].unsqueeze(1)\n",
        "        if self.use_position_input:\n",
        "            grid_points = data_dict['sdf_query_points']\n",
        "            input_grid_features = paddle.concat((input_grid_features, grid_points), axis=1)\n",
        "        output_points = data_dict['vertices']\n",
        "        return input_grid_features, output_points, data_dict[\"edges\"],data_dict[\"aug_features\"], None\n",
        "\n",
        "\n",
        "    @paddle.no_grad()\n",
        "    def eval_dict(self, data_dict, loss_fn=None, decode_fn=None, **kwargs):\n",
        "        use_hidden_states = True\n",
        "        input_grid_features, output_points, edges, aug_features, rag_pressure = self.data_dict_to_input(data_dict)\n",
        "        pred, hidden_states = self(output_points, edge_index=edges, input_grid_features=input_grid_features,\n",
        "                                   use_hidden_states=use_hidden_states, aug_features=aug_features,\n",
        "                                   rag_pressure=rag_pressure)\n",
        "\n",
        "        out_dict = {'pressure': pred, 'hidden_states': hidden_states, 'denormalized_pressure': decode_fn(pred)}\n",
        "        if loss_fn is not None:\n",
        "            gt = data_dict['pressure'].unsqueeze(-1)\n",
        "            out_dict['l2'] = loss_fn(pred, gt)\n",
        "            if decode_fn is not None:\n",
        "                pred_decoded = decode_fn(pred)\n",
        "                gt_decoded = decode_fn(gt)\n",
        "                out_dict['l2_decoded'] = loss_fn(pred_decoded, gt_decoded)\n",
        "        return out_dict\n",
        "\n",
        "    def loss_dict(self, data_dict, loss_fn=None, **kwargs):\n",
        "        input_grid_features, output_points, edges, aug_features, rag_pressure  = self.data_dict_to_input(data_dict)\n",
        "        pred, hidden_states = self(output_points, edge_index=edges, input_grid_features=input_grid_features,\n",
        "                                   aug_features=aug_features,  rag_pressure=rag_pressure)\n",
        "        gt = data_dict['pressure'].unsqueeze(-1)\n",
        "        if loss_fn is None:\n",
        "            loss_fn = F.mse_loss\n",
        "        return {'loss': loss_fn(pred, gt)}\n",
        "\n",
        "    def weighted_mse_loss(self, y_pred, y_true):\n",
        "        \"\"\"\n",
        "        y_pred: (batch_size, num_points, 1)\n",
        "        \"\"\"\n",
        "        y_pred = y_pred.squeeze(-1)\n",
        "        y_true = y_true.squeeze(-1)\n",
        "        # 计算权重\n",
        "        y_true_abs = paddle.abs(y_true)\n",
        "        y_min = paddle.min(y_true_abs, axis=-1)\n",
        "        y_max = paddle.max(y_true_abs, axis=-1)\n",
        "        weights = (y_true_abs - y_min) / (y_max - y_min) + 1\n",
        "\n",
        "        # 计算加权MSE损失\n",
        "        losses = weights * (y_pred - y_true) ** 2\n",
        "        return paddle.mean(losses)\n",
        "\n",
        "    def loss(self, data_dict, loss_fn=None, normalizer=None, **kwargs):\n",
        "        input_grid_features, output_points, edges, aug_features, rag_pressure = self.data_dict_to_input(data_dict)\n",
        "        pred, hidden_states = self(output_points, edge_index=edges, input_grid_features=input_grid_features,\n",
        "                                   aug_features=aug_features, rag_pressure=rag_pressure)\n",
        "        gt = data_dict['pressure'].unsqueeze(-1)\n",
        "        return loss_fn(pred, gt),  self.weighted_mse_loss(pred, gt), F.mse_loss(normalizer.decode(pred.squeeze(0)), normalizer.decode(gt.squeeze(0)))\n"
      ],
      "metadata": {
        "id": "oHE4lO25ozPI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "config_file = \"./drive/MyDrive/configs/SDF_GCN_FNO.yaml\"\n",
        "args = parse_args(config_file)\n",
        "config = load_config(config_file)\n",
        "\n",
        "for key, value in vars(args).items():\n",
        "    if key != \"config\" and value is not None:\n",
        "        config[key] = value\n",
        "\n",
        "model = SDFWithGCNFNO(\n",
        "            gcn_input_size=config.input_size,\n",
        "            gcn_hidden_size=config.gcn_hidden_size,\n",
        "            gcn_output_size=config.gcn_output_size,\n",
        "            gcnfno_modes=config.gcnfno_modes,\n",
        "            gcnfno_width=config.gcnfno_width,\n",
        "            fno_input_size=config.fno_input_size,\n",
        "            fno_output_size=config.fno_output_size,\n",
        "            final_output_size=config.final_output_size,\n",
        "            fno_modes=config.fno_modes,  # 新增配置项\n",
        "            fno_width=config.fno_width,  # 新增配置项\n",
        "            use_position_input=config.use_position_input,\n",
        "            add_attn=config.add_attn\n",
        "        )\n",
        "checkpoint = paddle.load(\"./drive/MyDrive/model.pdparams\")\n",
        "\n",
        "model.load_dict(checkpoint)\n",
        "print(\"\\n-------Starting Evaluation over [track A]--------\")\n",
        "t1 = default_timer()\n",
        "track = \"Track_A\"\n",
        "config.n_train = 1\n",
        "os.makedirs(f\"./output/{config.project_name}/{track}\", exist_ok=True)\n",
        "data_loader = CFDSDFDataModule(\n",
        "    config.data_dir,\n",
        "    config.n_train,\n",
        "    config.n_test,\n",
        "    spatial_resolution=config.sdf_spatial_resolution,\n",
        "    test_data_dir=config.test_data_dir,\n",
        "    use_aug_feature=config.use_aug_feature,\n",
        "    rag_data_dir=config.rag_data_dir,\n",
        ")\n",
        "eval_dict = eval(model, data_loader, config, loss_fn=None, track=track)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3CvqMA19o1Ew",
        "outputId": "599e09d5-0a53-4038-beb8-fe01514b6bb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "-------Starting Evaluation over [track A]--------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-204ba6483bd2>:504: UserWarning: 550 meshes are available, but 51 are requested.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fake pressure for data_test_A\n",
            "write : ./output/data_test_A_658.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_658.npy\n",
            "write : ./output/data_test_A_659.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_659.npy\n",
            "write : ./output/data_test_A_660.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_660.npy\n",
            "write : ./output/data_test_A_662.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_662.npy\n",
            "write : ./output/data_test_A_663.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_663.npy\n",
            "write : ./output/data_test_A_664.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_664.npy\n",
            "write : ./output/data_test_A_665.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_665.npy\n",
            "write : ./output/data_test_A_666.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_666.npy\n",
            "write : ./output/data_test_A_667.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_667.npy\n",
            "write : ./output/data_test_A_668.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_668.npy\n",
            "write : ./output/data_test_A_672.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_672.npy\n",
            "write : ./output/data_test_A_673.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_673.npy\n",
            "write : ./output/data_test_A_674.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_674.npy\n",
            "write : ./output/data_test_A_675.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_675.npy\n",
            "write : ./output/data_test_A_676.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_676.npy\n",
            "write : ./output/data_test_A_677.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_677.npy\n",
            "write : ./output/data_test_A_678.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_678.npy\n",
            "write : ./output/data_test_A_679.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_679.npy\n",
            "write : ./output/data_test_A_681.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_681.npy\n",
            "write : ./output/data_test_A_683.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_683.npy\n",
            "write : ./output/data_test_A_684.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_684.npy\n",
            "write : ./output/data_test_A_686.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_686.npy\n",
            "write : ./output/data_test_A_687.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_687.npy\n",
            "write : ./output/data_test_A_688.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_688.npy\n",
            "write : ./output/data_test_A_689.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_689.npy\n",
            "write : ./output/data_test_A_690.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_690.npy\n",
            "write : ./output/data_test_A_691.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_691.npy\n",
            "write : ./output/data_test_A_692.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_692.npy\n",
            "write : ./output/data_test_A_693.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_693.npy\n",
            "write : ./output/data_test_A_695.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_695.npy\n",
            "write : ./output/data_test_A_696.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_696.npy\n",
            "write : ./output/data_test_A_697.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_697.npy\n",
            "write : ./output/data_test_A_700.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_700.npy\n",
            "write : ./output/data_test_A_701.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_701.npy\n",
            "write : ./output/data_test_A_702.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_702.npy\n",
            "write : ./output/data_test_A_703.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_703.npy\n",
            "write : ./output/data_test_A_704.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_704.npy\n",
            "write : ./output/data_test_A_705.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_705.npy\n",
            "write : ./output/data_test_A_708.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_708.npy\n",
            "write : ./output/data_test_A_709.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_709.npy\n",
            "write : ./output/data_test_A_710.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_710.npy\n",
            "write : ./output/data_test_A_711.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_711.npy\n",
            "write : ./output/data_test_A_712.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_712.npy\n",
            "write : ./output/data_test_A_713.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_713.npy\n",
            "write : ./output/data_test_A_715.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_715.npy\n",
            "write : ./output/data_test_A_717.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_717.npy\n",
            "write : ./output/data_test_A_718.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_718.npy\n",
            "write : ./output/data_test_A_719.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_719.npy\n",
            "write : ./output/data_test_A_721.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_721.npy\n",
            "write : ./output/data_test_A_722.vtk\n",
            "saving *.npy file for [Track_A] leaderboard :  ./result/press_722.npy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Track B"
      ],
      "metadata": {
        "id": "EfreVnGhqUlu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget --header=\"Host: drive.usercontent.google.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Cookie: __Secure-ENID=12.SE=Yd0Bj-CLJ14fnd4qzdJHmwUs4B5zz46UaPC1cPJigNqqFV9PtM2CYyBpSbCkOyzUwzlEdZ1nZFf-igtGi7wSdJ_gqQSfQfh84r9egqFQAy9-GKayCRbdQKdera-2mkpuIT-c64CyR9vfNojM3hxZ9Dej-dGvtxlGjal9ttEHybw; __gsas=ID=ae0421b9a34b478c:T=1710758437:RT=1710758437:S=ALNI_MZP13R9ZOHbCzC0rgHSMrGXj6GCsg; HSID=A-4I-ZudDNUIB6EKH; SSID=A7v_1v9un6xAwVNku; APISID=ctK8IbLjeuDUmgys/AFnMSLWt9KddceDI6; SAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; __Secure-1PAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; __Secure-3PAPISID=J7GhTwED67EBqJJT/A9nwK7mr0ijGPw08r; SID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_kzuBV1TvOhAIC8VF1e9fpgACgYKATQSARQSFQHGX2Mi8LXUwWoIwNCEPU8Sy3mXUxoVAUF8yKqGXVfjTGz9gQal7nwGr4Pl0076; __Secure-1PSID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_PDa-DzVmbdGFPyxMQpk9_QACgYKAewSARQSFQHGX2MiAeee4fn0OWglWZfAygqkyBoVAUF8yKp-Sfmtnueimxc-0QbJRF9I0076; __Secure-3PSID=g.a000kgiBabgKCiCYKve9zfoWVgz9eu8sBA6N4XDPPpP5pcW16_C_g9IrMeU98APBo9Stp6wEnAACgYKAQASARQSFQHGX2MiFWtc9ucONXnpxBzlRdudEhoVAUF8yKoeZwCpJDnjfAFjGssHSUGm0076; NID=515=GQhY9nKKFCx3qFDjE0MA4ubjWNdef6xCIY_RfWOPWKEtyfBN3nAUl8WHI2VczjNQ4rVkj1XBAY8WNWHXyqSK10CfT4FxsFlPzrHIJpeTtm1nWRNBd9AAfBKJHz4XpESszntVUTE_59RklZuKo0vk1poReVi2da1PZKC3CTKH2Ll3gB5xuB9wf4bmq8ylVUuIROPJczr0XnCuUHV3qLdBvgy9_870b6UwOq1iOlIxFQFm01EZ4pqF4q1Ub3QRSWpEMLh4LSZFpJ5O255R5OV7krmEdDvH_sHoTEPZAg2PoEpwAyGK6Xp9qcLIlldgx5-5V86N8Wtb93uTlQuA_CFXb5_2eP3bgeX8txwlJ5SrldVjg9ctzYtBU2RwJKTSvdHfIG7lpOkg6XlkvDOcJpR3DihT_OlqnPn7drCAJpvVDv29hZn5XPMXaSrNdbG64OJ9urJEw5odEwsLYkkpC1vmlUcuoo52S5f6RQu0Z8kZiV8iRW6XIqHsSmQHunVaxk6xWCStUg; __Secure-1PSIDTS=sidts-CjEB3EgAEtTS0OazynCofIH4RCBstiRP5flEcvYW3z4Fg9oGd5QOESDOZt1wO2iqUYHjEAA; __Secure-3PSIDTS=sidts-CjEB3EgAEtTS0OazynCofIH4RCBstiRP5flEcvYW3z4Fg9oGd5QOESDOZt1wO2iqUYHjEAA; SIDCC=AKEyXzVI6aMX8lSDja86Yts3FBAtBzPCzVNgaX5BCz78NWsWzlT3yFWKUV7ZE46SFzE1GiBI-cHdTw; __Secure-1PSIDCC=AKEyXzUo4NQAwqqPMxP2eye-MFEbZmBIm_sZqRU1amttg0YoQkc8ZKSNXdHl5jNCMEbhrUHhS9-K; __Secure-3PSIDCC=AKEyXzWf2lIdmDLeZKpXSi9GytVQb6XudrYiNUBA5gW952YuLh8kL6T3IbBlu8zOTfGEcdUp5O1R\" --header=\"Connection: keep-alive\" \"https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=dc3aa13c-c3a9-458f-983a-8586798cb635&at=APZUnTX25XMxi-z-3wBcgR93IGsL%3A1719235792953\" -c -O 'Dataset.zip'\n",
        "!unzip Dataset.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zrdb-SJ0qQVq",
        "outputId": "264a3c36-1559-4e40-c7c7-5d19f72339bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-23 12:57:39--  https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=dc3aa13c-c3a9-458f-983a-8586798cb635&at=APZUnTX25XMxi-z-3wBcgR93IGsL%3A1719235792953\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 142.251.2.132, 2607:f8b0:4023:c0d::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|142.251.2.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1084182095 (1.0G) [application/octet-stream]\n",
            "Saving to: ‘Dataset.zip’\n",
            "\n",
            "Dataset.zip         100%[===================>]   1.01G  79.0MB/s    in 14s     \n",
            "\n",
            "2024-07-23 12:57:55 (72.2 MB/s) - ‘Dataset.zip’ saved [1084182095/1084182095]\n",
            "\n",
            "Archive:  Dataset.zip\n",
            "   creating: Dataset/\n",
            "   creating: Dataset/Testset_track_A/\n",
            "   creating: Dataset/Testset_track_A/Inference/\n",
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            "   creating: Dataset/Testset_track_B/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/1638f9c292b9437bb46885186407a63e584856c91f9f4c18908b87abd46471e0?responseContentDisposition=attachment%3B%20filename%3Dtrack_B.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-05-05T03%3A02%3A25Z%2F-1%2F%2Fcfdfd6b6a9e096c761ee8e7d863d586741c69a9e6de89f9c3696706d35f8b265\" -c -O 'track_B.zip'\n",
        "!wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/2dddd05e577849ad95e1fe1133d3af29d13085ac0cfd499c853ff5d9df2ac07f?responseContentDisposition=attachment%3B%20filename%3Dtrain_data.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-05-05T03%3A23%3A33Z%2F-1%2F%2F8540633c7e39fddf8471d6d8206c3b761748c58c06005acb218593a8df19d7f1\" -c -O 'train_data.zip'\n",
        "!wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/a96dc8ba8201445b966980a0a48f52705338a48e29e64c53bddb7ef8861c5123?responseContentDisposition=attachment%3B%20filename%3Dtrack_A.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-05-06T07%3A54%3A51Z%2F-1%2F%2F17b5155bd16a8af1e4af971498082687656af7fcecfc5a8e57591b85053210ec\" -c -O 'track_A.zip'\n",
        "!wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/dcaba9f0e87549e395e1682a4a0a43c547a896034e3f4417a4d10ba85a949944?responseContentDisposition=attachment%3B%20filename%3DPaddleScience.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-05-11T16%3A01%3A57Z%2F-1%2F%2Fb2149791689d3b19c02a86782229901cb75743d07c7163dfa185fea286cd1f02\" -c -O 'PaddleScience.zip'\n",
        "!wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/38e9adf0fce84527aad3558cc3e82d0e9a251aac4c934297afae9b74d9b3d1e9?responseContentDisposition=attachment%3B%20filename%3Dtrain_track_B.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-04T03%3A21%3A02Z%2F-1%2F%2Facd359add161bace603a52c7a268467406cb3c1889a7114bbb687de8002b55f6\" -c -O 'train_track_B.zip'\n",
        "!wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/7877c2fd300345599ed3365feda50425f4857caa71bf4af9bf047fb08e35aa97?responseContentDisposition=attachment%3B%20filename%3Dmesh_B_0603.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-04T11%3A48%3A26Z%2F-1%2F%2Fec6cce492ba6afff841ef197860065742a7ba8220def02c1cbc311333aa993b2\" -c -O 'mesh_B_0603.zip'\n",
        "!wget --header=\"Host: ai-studio-online.bj.bcebos.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36 Edg/126.0.0.0\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6\" --header=\"Referer: https://aistudio.baidu.com/\" \"https://ai-studio-online.bj.bcebos.com/v1/a02dba5700974c6a811f579fff216ccf9a4129b849994dcca99390b222c28572?responseContentDisposition=attachment%3B%20filename%3D3rd_lib.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-08T05%3A29%3A20Z%2F-1%2F%2Fef71819149664a3c8438d3dfd02544e77cdb4fda679efbdd402b4e2c77a2c06d\" -c -O '3rd_lib.zip'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QPQ9QXeqaYT",
        "outputId": "40039367-edba-422a-b492-f50eae40a6b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-23 12:58:13--  https://ai-studio-online.bj.bcebos.com/v1/1638f9c292b9437bb46885186407a63e584856c91f9f4c18908b87abd46471e0?responseContentDisposition=attachment%3B%20filename%3Dtrack_B.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-05-05T03%3A02%3A25Z%2F-1%2F%2Fcfdfd6b6a9e096c761ee8e7d863d586741c69a9e6de89f9c3696706d35f8b265\n",
            "Resolving ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)... 103.235.47.176, 2409:8c04:1001:1203:0:ff:b0bb:4f27\n",
            "Connecting to ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)|103.235.47.176|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1012191818 (965M) [application/octet-stream]\n",
            "Saving to: ‘track_B.zip’\n",
            "\n",
            "track_B.zip         100%[===================>] 965.30M  13.1MB/s    in 84s     \n",
            "\n",
            "2024-07-23 12:59:39 (11.6 MB/s) - ‘track_B.zip’ saved [1012191818/1012191818]\n",
            "\n",
            "--2024-07-23 12:59:39--  https://ai-studio-online.bj.bcebos.com/v1/2dddd05e577849ad95e1fe1133d3af29d13085ac0cfd499c853ff5d9df2ac07f?responseContentDisposition=attachment%3B%20filename%3Dtrain_data.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-05-05T03%3A23%3A33Z%2F-1%2F%2F8540633c7e39fddf8471d6d8206c3b761748c58c06005acb218593a8df19d7f1\n",
            "Resolving ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)... 36.110.192.178, 2409:8c04:1001:1203:0:ff:b0bb:4f27\n",
            "Connecting to ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)|36.110.192.178|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 46031310 (44M) [application/octet-stream]\n",
            "Saving to: ‘train_data.zip’\n",
            "\n",
            "train_data.zip      100%[===================>]  43.90M  8.65MB/s    in 14s     \n",
            "\n",
            "2024-07-23 12:59:54 (3.24 MB/s) - ‘train_data.zip’ saved [46031310/46031310]\n",
            "\n",
            "--2024-07-23 12:59:54--  https://ai-studio-online.bj.bcebos.com/v1/a96dc8ba8201445b966980a0a48f52705338a48e29e64c53bddb7ef8861c5123?responseContentDisposition=attachment%3B%20filename%3Dtrack_A.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-05-06T07%3A54%3A51Z%2F-1%2F%2F17b5155bd16a8af1e4af971498082687656af7fcecfc5a8e57591b85053210ec\n",
            "Resolving ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)... 36.110.192.178, 2409:8c04:1001:1203:0:ff:b0bb:4f27\n",
            "Connecting to ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)|36.110.192.178|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4688102 (4.5M) [application/octet-stream]\n",
            "Saving to: ‘track_A.zip’\n",
            "\n",
            "track_A.zip         100%[===================>]   4.47M  1.08MB/s    in 8.1s    \n",
            "\n",
            "2024-07-23 13:00:03 (569 KB/s) - ‘track_A.zip’ saved [4688102/4688102]\n",
            "\n",
            "--2024-07-23 13:00:03--  https://ai-studio-online.bj.bcebos.com/v1/dcaba9f0e87549e395e1682a4a0a43c547a896034e3f4417a4d10ba85a949944?responseContentDisposition=attachment%3B%20filename%3DPaddleScience.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-05-11T16%3A01%3A57Z%2F-1%2F%2Fb2149791689d3b19c02a86782229901cb75743d07c7163dfa185fea286cd1f02\n",
            "Resolving ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)... 36.110.192.178, 2409:8c04:1001:1203:0:ff:b0bb:4f27\n",
            "Connecting to ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)|36.110.192.178|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4524150 (4.3M) [application/octet-stream]\n",
            "Saving to: ‘PaddleScience.zip’\n",
            "\n",
            "PaddleScience.zip   100%[===================>]   4.31M  3.30MB/s    in 1.3s    \n",
            "\n",
            "2024-07-23 13:00:05 (3.30 MB/s) - ‘PaddleScience.zip’ saved [4524150/4524150]\n",
            "\n",
            "--2024-07-23 13:00:05--  https://ai-studio-online.bj.bcebos.com/v1/38e9adf0fce84527aad3558cc3e82d0e9a251aac4c934297afae9b74d9b3d1e9?responseContentDisposition=attachment%3B%20filename%3Dtrain_track_B.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-04T03%3A21%3A02Z%2F-1%2F%2Facd359add161bace603a52c7a268467406cb3c1889a7114bbb687de8002b55f6\n",
            "Resolving ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)... 36.110.192.178, 2409:8c04:1001:1203:0:ff:b0bb:4f27\n",
            "Connecting to ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)|36.110.192.178|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4740031429 (4.4G) [application/octet-stream]\n",
            "Saving to: ‘train_track_B.zip’\n",
            "\n",
            "train_track_B.zip   100%[===================>]   4.41G  22.4MB/s    in 3m 38s  \n",
            "\n",
            "2024-07-23 13:03:44 (20.8 MB/s) - ‘train_track_B.zip’ saved [4740031429/4740031429]\n",
            "\n",
            "--2024-07-23 13:03:44--  https://ai-studio-online.bj.bcebos.com/v1/7877c2fd300345599ed3365feda50425f4857caa71bf4af9bf047fb08e35aa97?responseContentDisposition=attachment%3B%20filename%3Dmesh_B_0603.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-04T11%3A48%3A26Z%2F-1%2F%2Fec6cce492ba6afff841ef197860065742a7ba8220def02c1cbc311333aa993b2\n",
            "Resolving ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)... 103.235.47.176, 2409:8c04:1001:1203:0:ff:b0bb:4f27\n",
            "Connecting to ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)|103.235.47.176|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4217480891 (3.9G) [application/octet-stream]\n",
            "Saving to: ‘mesh_B_0603.zip’\n",
            "\n",
            "mesh_B_0603.zip     100%[===================>]   3.93G  13.4MB/s    in 4m 47s  \n",
            "\n",
            "2024-07-23 13:08:33 (14.0 MB/s) - ‘mesh_B_0603.zip’ saved [4217480891/4217480891]\n",
            "\n",
            "--2024-07-23 13:08:33--  https://ai-studio-online.bj.bcebos.com/v1/a02dba5700974c6a811f579fff216ccf9a4129b849994dcca99390b222c28572?responseContentDisposition=attachment%3B%20filename%3D3rd_lib.zip&authorization=bce-auth-v1%2F5cfe9a5e1454405eb2a975c43eace6ec%2F2024-06-08T05%3A29%3A20Z%2F-1%2F%2Fef71819149664a3c8438d3dfd02544e77cdb4fda679efbdd402b4e2c77a2c06d\n",
            "Resolving ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)... 103.235.47.176, 2409:8c04:1001:1203:0:ff:b0bb:4f27\n",
            "Connecting to ai-studio-online.bj.bcebos.com (ai-studio-online.bj.bcebos.com)|103.235.47.176|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 612695041 (584M) [application/octet-stream]\n",
            "Saving to: ‘3rd_lib.zip’\n",
            "\n",
            "3rd_lib.zip         100%[===================>] 584.31M  11.9MB/s    in 60s     \n",
            "\n",
            "2024-07-23 13:09:35 (9.76 MB/s) - ‘3rd_lib.zip’ saved [612695041/612695041]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip 3rd_lib.zip\n",
        "!unzip PaddleScience.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WARjo0cQqcrI",
        "outputId": "7fd543cf-7c4b-4f33-a58d-b430728bfcd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43m流式输出内容被截断，只能显示最后 5000 行内容。\u001b[0m\n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/__pycache__/_h_h_e_a.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/__pycache__/T_S_I_D_.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/C_P_A_L_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/S_T_A_T_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/T_S_I__3.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/G_P_K_G_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_g_l_y_f.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_m_e_t_a.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/C_F_F_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/otData.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/__init__.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_f_v_a_r.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/sbixStrike.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/O_S_2f_2.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_o_p_b_d.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_v_m_t_x.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_c_v_a_r.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/V_O_R_G_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/M_A_T_H_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/J_S_T_F_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_s_b_i_x.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_m_o_r_t.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_l_o_c_a.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/table_API_readme.txt  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/L_T_S_H_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/T_S_I_V_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/grUtils.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_g_a_s_p.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_m_a_x_p.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_t_r_a_k.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/M_V_A_R_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_g_v_a_r.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/G_M_A_P_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/D_S_I_G_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_n_a_m_e.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_a_n_k_r.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_l_c_a_r.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/D__e_b_g.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_a_v_a_r.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_p_r_e_p.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/S__i_l_l.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_b_s_l_n.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/E_B_L_C_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_p_r_o_p.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/otTables.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_g_c_i_d.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/asciiTable.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/T_S_I__5.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/TupleVariation.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/ttProgram.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/G_D_E_F_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/S_V_G_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/C_F_F__2.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/T_S_I__0.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/V_V_A_R_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/S__i_l_f.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_k_e_r_n.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/E_B_D_T_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/G__l_a_t.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/T_S_I_S_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/T_S_I__1.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/T_S_I__2.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/T_S_I_B_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/T_S_I_P_.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_l_t_a_g.py  \n",
            "  inflating: 3rd_lib/fontTools/ttLib/tables/_f_e_a_t.py  \n",
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            "  inflating: 3rd_lib/open3d/examples/t_reconstruction_system/dense_slam_gui.py  \n",
            "  inflating: 3rd_lib/open3d/examples/t_reconstruction_system/rgbd_odometry.py  \n",
            "  inflating: 3rd_lib/open3d/examples/t_reconstruction_system/dense_slam.py  \n",
            "  inflating: 3rd_lib/open3d/examples/t_reconstruction_system/run_system.py  \n",
            "  inflating: 3rd_lib/open3d/examples/t_reconstruction_system/pose_graph_optim.py  \n",
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            "  inflating: 3rd_lib/open3d/examples/t_reconstruction_system/default_intrinsics.json  \n",
            "  inflating: 3rd_lib/open3d/examples/t_reconstruction_system/integrate_custom.py  \n",
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            "  inflating: 3rd_lib/open3d/labextension/static/style.js  \n",
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            "   creating: 3rd_lib/attrs-23.2.0.dist-info/\n",
            "  inflating: 3rd_lib/attrs-23.2.0.dist-info/WHEEL  \n",
            "   creating: 3rd_lib/attrs-23.2.0.dist-info/licenses/\n",
            "  inflating: 3rd_lib/attrs-23.2.0.dist-info/licenses/LICENSE  \n",
            " extracting: 3rd_lib/attrs-23.2.0.dist-info/INSTALLER  \n",
            "  inflating: 3rd_lib/attrs-23.2.0.dist-info/RECORD  \n",
            "  inflating: 3rd_lib/attrs-23.2.0.dist-info/METADATA  \n",
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            "   creating: 3rd_lib/tzdata/\n",
            "   creating: 3rd_lib/tzdata/zoneinfo/\n",
            "   creating: 3rd_lib/tzdata/zoneinfo/__pycache__/\n",
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            " extracting: 3rd_lib/tzdata/zoneinfo/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/GMT-0  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/ROK  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Australia/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Adelaide  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Darwin  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Queensland  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/Australia/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/North  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Melbourne  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Eucla  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Tasmania  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Brisbane  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/South  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/West  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/LHI  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Australia/__pycache__/\n",
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            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Perth  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Lord_Howe  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Canberra  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Sydney  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Lindeman  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Broken_Hill  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Yancowinna  \n",
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            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Victoria  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/NSW  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Currie  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Australia/Hobart  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Etc/\n",
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            "  inflating: 3rd_lib/tzdata/zoneinfo/Etc/GMT+8  \n",
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            " extracting: 3rd_lib/tzdata/zoneinfo/Etc/__init__.py  \n",
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            "   creating: 3rd_lib/tzdata/zoneinfo/Etc/__pycache__/\n",
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            "  inflating: 3rd_lib/tzdata/zoneinfo/Etc/Universal  \n",
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            "  inflating: 3rd_lib/tzdata/zoneinfo/Iran  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Canada/\n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Canada/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Canada/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Canada/Newfoundland  \n",
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            "  inflating: 3rd_lib/tzdata/zoneinfo/Canada/Eastern  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Canada/Yukon  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/Canada/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Canada/Atlantic  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Canada/Pacific  \n",
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            "   creating: 3rd_lib/tzdata/zoneinfo/Pacific/\n",
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            " extracting: 3rd_lib/tzdata/zoneinfo/Pacific/__init__.py  \n",
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            "   creating: 3rd_lib/tzdata/zoneinfo/Pacific/__pycache__/\n",
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            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Bougainville  \n",
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            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Galapagos  \n",
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            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Efate  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Enderbury  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Niue  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Pohnpei  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Auckland  \n",
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            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Tarawa  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Nauru  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Apia  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Guadalcanal  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Kanton  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Kiritimati  \n",
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            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Pago_Pago  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Kwajalein  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Noumea  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Honolulu  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Wake  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Chatham  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Samoa  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Majuro  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Saipan  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Wallis  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Funafuti  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Yap  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Pacific/Guam  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/GMT0  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Jamaica  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/PST8PDT  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Chile/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Chile/EasterIsland  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Chile/Continental  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Chile/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Chile/__pycache__/__init__.cpython-310.pyc  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/Chile/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/NZ  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Iceland  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/PRC  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Asia/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Tashkent  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Nicosia  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Choibalsan  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Qatar  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Bahrain  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Muscat  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Amman  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Magadan  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Taipei  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Kuala_Lumpur  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Damascus  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Qyzylorda  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Tokyo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Tehran  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Oral  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Jakarta  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Ulan_Bator  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Kuwait  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Urumqi  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Baghdad  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Chita  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Dhaka  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Yakutsk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Tel_Aviv  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Samarkand  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Colombo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Baku  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Atyrau  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Rangoon  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Sakhalin  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Dushanbe  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Saigon  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Ashkhabad  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Istanbul  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Aqtau  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Brunei  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Khandyga  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Novokuznetsk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Yangon  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Macao  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Jerusalem  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Vladivostok  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Krasnoyarsk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Harbin  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Ashgabat  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Kamchatka  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Anadyr  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Hong_Kong  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Bishkek  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Seoul  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Asia/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Qostanay  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Pyongyang  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Dacca  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Phnom_Penh  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Aqtobe  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Jayapura  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Katmandu  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Famagusta  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Dubai  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Thimphu  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Kathmandu  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Beirut  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Manila  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Chongqing  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Dili  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Ho_Chi_Minh  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Ust-Nera  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Ulaanbaatar  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Riyadh  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Pontianak  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Irkutsk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Chungking  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Kabul  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Vientiane  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Srednekolymsk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Hovd  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Omsk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Kuching  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Novosibirsk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Gaza  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Calcutta  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Hebron  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Kolkata  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Yekaterinburg  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Barnaul  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Kashgar  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Yerevan  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Almaty  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Ujung_Pandang  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Tomsk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Karachi  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Singapore  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Thimbu  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Makassar  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Shanghai  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/Asia/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Aden  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Macau  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Tbilisi  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Asia/Bangkok  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/zonenow.tab  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Africa/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Luanda  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Ouagadougou  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Mogadishu  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Sao_Tome  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Lubumbashi  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Douala  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Lome  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Monrovia  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Addis_Ababa  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Bamako  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Casablanca  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Timbuktu  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Libreville  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Kampala  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Dar_es_Salaam  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Porto-Novo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Djibouti  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Nairobi  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Bissau  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Lusaka  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Cairo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Tunis  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Maputo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Johannesburg  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Dakar  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Bujumbura  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Harare  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Gaborone  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Tripoli  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Maseru  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Juba  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Kigali  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Asmera  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Malabo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Bangui  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Blantyre  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Lagos  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Nouakchott  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/Africa/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/El_Aaiun  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Freetown  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Africa/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Ndjamena  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Mbabane  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Algiers  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Brazzaville  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Niamey  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Accra  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Abidjan  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Windhoek  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Conakry  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Banjul  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Ceuta  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Khartoum  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Kinshasa  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Africa/Asmara  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/EET  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Atlantic/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/Cape_Verde  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/Madeira  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/Bermuda  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/Stanley  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/South_Georgia  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/Atlantic/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/St_Helena  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/Faeroe  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/Canary  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/Faroe  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/Jan_Mayen  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/Reykjavik  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/Azores  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Atlantic/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Atlantic/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/zone1970.tab  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/NZ-CHAT  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/GB  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Portugal  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/GB-Eire  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/GMT+0  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Universal  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/ROC  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/WET  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/HST  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/EST  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Kwajalein  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Brazil/\n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Brazil/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Brazil/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Brazil/East  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/Brazil/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Brazil/Acre  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Brazil/West  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Brazil/DeNoronha  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Factory  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/CST6CDT  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Cuba  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Egypt  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Eire  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/MST7MDT  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Mexico/\n",
            " extracting: 3rd_lib/tzdata/zoneinfo/Mexico/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Mexico/BajaSur  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Mexico/General  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Mexico/BajaNorte  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Mexico/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Mexico/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Greenwich  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/EST5EDT  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Arctic/\n",
            " extracting: 3rd_lib/tzdata/zoneinfo/Arctic/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Arctic/Longyearbyen  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Arctic/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Arctic/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/CET  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Navajo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Hongkong  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Singapore  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/America/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Whitehorse  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Panama  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Atikokan  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Halifax  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Goose_Bay  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Curacao  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Asuncion  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Martinique  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Grenada  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Rankin_Inlet  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Bahia_Banderas  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Mazatlan  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Detroit  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Santiago  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Denver  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Anchorage  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Boise  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Jamaica  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Juneau  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Inuvik  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Managua  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Vancouver  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Cancun  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Eirunepe  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/St_Johns  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Noronha  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Belem  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Ciudad_Juarez  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Guadeloupe  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Araguaina  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Recife  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Coral_Harbour  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Yakutat  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Manaus  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Toronto  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Louisville  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Dominica  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Caracas  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Chihuahua  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Glace_Bay  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Nassau  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Iqaluit  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Belize  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Fortaleza  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/America/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Cordoba  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Ojinaga  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/America/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Fort_Wayne  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Scoresbysund  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Rainy_River  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/St_Barthelemy  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Creston  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Guayaquil  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/St_Kitts  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/St_Lucia  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Blanc-Sablon  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/America/Kentucky/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Kentucky/Louisville  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Kentucky/Monticello  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/America/Kentucky/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Kentucky/__pycache__/__init__.cpython-310.pyc  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/America/Kentucky/__init__.py  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/America/North_Dakota/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/North_Dakota/New_Salem  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/North_Dakota/Beulah  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/America/North_Dakota/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/North_Dakota/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/North_Dakota/Center  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/America/North_Dakota/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/New_York  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Swift_Current  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Aruba  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Maceio  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Nome  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Nuuk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Godthab  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Catamarca  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Port-au-Prince  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Porto_Velho  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Cayman  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Menominee  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Dawson_Creek  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Cayenne  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Anguilla  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Pangnirtung  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Kralendijk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Rio_Branco  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Port_of_Spain  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Costa_Rica  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/America/Argentina/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/ComodRivadavia  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/America/Argentina/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/Ushuaia  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/Rio_Gallegos  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/San_Juan  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/Cordoba  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/Mendoza  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/La_Rioja  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/Salta  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/Catamarca  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/America/Argentina/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/Tucuman  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/Jujuy  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/Buenos_Aires  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Argentina/San_Luis  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Buenos_Aires  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Porto_Acre  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Guatemala  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Bogota  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Ensenada  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Cambridge_Bay  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Shiprock  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Guyana  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Campo_Grande  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Montreal  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Adak  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Bahia  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Miquelon  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Jujuy  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Atka  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Merida  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Santarem  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Paramaribo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Winnipeg  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Lima  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Tegucigalpa  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Monterrey  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Mendoza  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Metlakatla  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Sao_Paulo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Dawson  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/America/Indiana/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Indiana/Knox  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/America/Indiana/__init__.py  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/America/Indiana/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Indiana/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Indiana/Indianapolis  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Indiana/Tell_City  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Indiana/Winamac  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Indiana/Vincennes  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Indiana/Petersburg  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Indiana/Vevay  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Indiana/Marengo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/St_Vincent  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Marigot  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Moncton  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Thunder_Bay  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Tortola  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Indianapolis  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Chicago  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Los_Angeles  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Virgin  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Puerto_Rico  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Lower_Princes  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Mexico_City  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Thule  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Rosario  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Montserrat  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Santa_Isabel  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Phoenix  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Nipigon  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Grand_Turk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Montevideo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Fort_Nelson  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Resolute  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Regina  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Boa_Vista  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Santo_Domingo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/St_Thomas  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Punta_Arenas  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Cuiaba  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Matamoros  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Yellowknife  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/La_Paz  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Antigua  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Edmonton  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/El_Salvador  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Knox_IN  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Hermosillo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Danmarkshavn  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Barbados  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Havana  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Sitka  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/America/Tijuana  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/iso3166.tab  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Antarctica/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/Casey  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/Vostok  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/McMurdo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/Mawson  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/Davis  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Antarctica/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/Rothera  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/Antarctica/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/Macquarie  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/Troll  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/Palmer  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/South_Pole  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/Syowa  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Antarctica/DumontDUrville  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/MET  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Europe/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Ljubljana  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Athens  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Luxembourg  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Helsinki  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Astrakhan  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Gibraltar  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Sofia  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Malta  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/London  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Zagreb  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Prague  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Belfast  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Warsaw  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Saratov  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Kirov  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Volgograd  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Berlin  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Jersey  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Riga  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Zurich  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Isle_of_Man  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Ulyanovsk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Dublin  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Chisinau  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Madrid  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/San_Marino  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Bucharest  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Zaporozhye  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Mariehamn  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Brussels  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Belgrade  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Europe/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Kaliningrad  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Budapest  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Minsk  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Andorra  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Oslo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Istanbul  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Uzhgorod  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Paris  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/Europe/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Vienna  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Guernsey  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Busingen  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Kiev  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Nicosia  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Amsterdam  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Bratislava  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Kyiv  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Monaco  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Stockholm  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Lisbon  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Tirane  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Rome  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Sarajevo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Vatican  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Moscow  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Simferopol  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Copenhagen  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Vaduz  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Podgorica  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Tiraspol  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Samara  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Skopje  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Vilnius  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Europe/Tallinn  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Israel  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Indian/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Indian/Kerguelen  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Indian/Mauritius  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Indian/Cocos  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Indian/Comoro  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/Indian/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Indian/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Indian/Reunion  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Indian/Antananarivo  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Indian/Maldives  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/Indian/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Indian/Mayotte  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Indian/Mahe  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Indian/Christmas  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Indian/Chagos  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/MST  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/zone.tab  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/UTC  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/GMT  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/W-SU  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Japan  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/UCT  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/US/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/Michigan  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/Alaska  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/Samoa  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/Arizona  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/Central  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/Eastern  \n",
            " extracting: 3rd_lib/tzdata/zoneinfo/US/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/Mountain  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/Pacific  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/Hawaii  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/East-Indiana  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/Aleutian  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/Indiana-Starke  \n",
            "   creating: 3rd_lib/tzdata/zoneinfo/US/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/US/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/zoneinfo/Poland  \n",
            "   creating: 3rd_lib/tzdata/__pycache__/\n",
            "  inflating: 3rd_lib/tzdata/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/tzdata/__init__.py  \n",
            "  inflating: 3rd_lib/tzdata/zones    \n",
            "   creating: 3rd_lib/matplotlib/\n",
            "  inflating: 3rd_lib/matplotlib/__init__.pyi  \n",
            "  inflating: 3rd_lib/matplotlib/gridspec.pyi  \n",
            "  inflating: 3rd_lib/matplotlib/font_manager.pyi  \n",
            "  inflating: 3rd_lib/matplotlib/offsetbox.pyi  \n",
            "  inflating: 3rd_lib/matplotlib/_constrained_layout.py  \n",
            "  inflating: 3rd_lib/matplotlib/_c_internal_utils.pyi  \n",
            "  inflating: 3rd_lib/matplotlib/artist.py  \n",
            "   creating: 3rd_lib/matplotlib/backends/\n",
            "  inflating: 3rd_lib/matplotlib/backends/backend_cairo.py  \n",
            "  inflating: 3rd_lib/matplotlib/backends/backend_wxagg.py  \n",
            "  inflating: 3rd_lib/matplotlib/backends/backend_qt.py  \n",
            "  inflating: 3rd_lib/matplotlib/backends/backend_qt5agg.py  \n",
            "  inflating: 3rd_lib/matplotlib/backends/backend_svg.py  \n",
            "  inflating: 3rd_lib/matplotlib/backends/backend_ps.py  \n",
            "  inflating: 3rd_lib/matplotlib/backends/_backend_tk.py  \n",
            "  inflating: 3rd_lib/matplotlib/backends/_backend_pdf_ps.py  \n",
            "  inflating: 3rd_lib/matplotlib/backends/backend_agg.py  \n",
            " extracting: 3rd_lib/matplotlib/backends/_backend_agg.pyi  \n",
            "  inflating: 3rd_lib/matplotlib/backends/_tkagg.cpython-310-x86_64-linux-gnu.so  \n",
            "  inflating: 3rd_lib/matplotlib/backends/backend_qtcairo.py  \n",
            "  inflating: 3rd_lib/matplotlib/backends/backend_gtk3cairo.py  \n",
            "   creating: 3rd_lib/matplotlib/backends/web_backend/\n",
            "  inflating: 3rd_lib/matplotlib/backends/web_backend/single_figure.html  \n",
            "   creating: 3rd_lib/matplotlib/backends/web_backend/css/\n",
            "  inflating: 3rd_lib/matplotlib/backends/web_backend/css/fbm.css  \n",
            "  inflating: 3rd_lib/matplotlib/backends/web_backend/css/mpl.css  \n",
            "  inflating: 3rd_lib/matplotlib/backends/web_backend/css/page.css  \n",
            "  inflating: 3rd_lib/matplotlib/backends/web_backend/css/boilerplate.css  \n",
            "  inflating: 3rd_lib/matplotlib/backends/web_backend/all_figures.html  \n",
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            " extracting: 3rd_lib/matplotlib/mpl-data/images/home.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/help-symbolic.svg  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/forward.pdf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/move.svg  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/zoom_to_rect.pdf  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/zoom_to_rect_large.png  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/move.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/back.pdf  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/subplots.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/forward-symbolic.svg  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/move.pdf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/help.svg  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/matplotlib.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/help.png  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/matplotlib_large.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/filesave-symbolic.svg  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/filesave_large.png  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/filesave.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/forward.svg  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/filesave.svg  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/hand.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/back.svg  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/subplots_large.png  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/forward_large.png  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/zoom_to_rect.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/matplotlib.svg  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/qt4_editor_options_large.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/home.pdf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/hand.pdf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/home.svg  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/qt4_editor_options.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/move-symbolic.svg  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/back.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/subplots-symbolic.svg  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/filesave.pdf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/subplots.pdf  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/help_large.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/matplotlib.pdf  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/move_large.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/qt4_editor_options.pdf  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/back_large.png  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/forward.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/zoom_to_rect-symbolic.svg  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/home-symbolic.svg  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/zoom_to_rect.svg  \n",
            " extracting: 3rd_lib/matplotlib/mpl-data/images/home_large.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/images/qt4_editor_options.svg  \n",
            "   creating: 3rd_lib/matplotlib/mpl-data/plot_directive/\n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/plot_directive/plot_directive.css  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/kpsewhich.lua  \n",
            "   creating: 3rd_lib/matplotlib/mpl-data/stylelib/\n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/Solarize_Light2.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8-dark.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8-dark-palette.mplstyle  \n",
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            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8-poster.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/fast.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/_mpl-gallery.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/bmh.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8-deep.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8-notebook.mplstyle  \n",
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            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/ggplot.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/_mpl-gallery-nogrid.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/classic.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8-white.mplstyle  \n",
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            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8-bright.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8-paper.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/fivethirtyeight.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8-colorblind.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/dark_background.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8-ticks.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8-muted.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/seaborn-v0_8-talk.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/grayscale.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/stylelib/tableau-colorblind10.mplstyle  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/matplotlibrc  \n",
            "   creating: 3rd_lib/matplotlib/mpl-data/fonts/\n",
            "   creating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/\n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/ZapfDingbats.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Times-Roman.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Symbol.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Courier-BoldOblique.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Courier.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Times-BoldItalic.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Times-Italic.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Courier-Bold.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/readme.txt  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Helvetica-Bold.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Times-Bold.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Courier-Oblique.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Helvetica.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Helvetica-BoldOblique.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/pdfcorefonts/Helvetica-Oblique.afm  \n",
            "   creating: 3rd_lib/matplotlib/mpl-data/fonts/afm/\n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/phvbo8an.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pbkli8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pagd8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pagdo8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pncb8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/phvb8an.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pcrr8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pbkl8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/phvr8an.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/phvlo8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/putri8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/cmr10.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/ptmr8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/phvbo8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pcrro8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/putbi8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/cmmi10.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pagko8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/cmtt10.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/ptmri8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pplr8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/putr8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pbkdi8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pncbi8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pplb8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/phvro8an.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pplbi8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/cmsy10.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pzcmi8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pbkd8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pcrbo8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/phvb8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pncr8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/phvr8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/putb8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/cmex10.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pzdr.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pagk8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/phvl8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pncri8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pcrb8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/psyr.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/pplri8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/ptmbi8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/ptmb8a.afm  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/afm/phvro8a.afm  \n",
            "   creating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/\n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/LICENSE_DEJAVU  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/cmss10.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/cmex10.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/cmr10.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/cmb10.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/LICENSE_STIX  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf  \n",
            "   creating: 3rd_lib/matplotlib/mpl-data/sample_data/\n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/topobathy.npz  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/s1045.ima.gz  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/jacksboro_fault_dem.npz  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/embedding_in_wx3.xrc  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/README.txt  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/goog.npz  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/grace_hopper.jpg  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/Stocks.csv  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/membrane.dat  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/data_x_x2_x3.csv  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/Minduka_Present_Blue_Pack.png  \n",
            "   creating: 3rd_lib/matplotlib/mpl-data/sample_data/axes_grid/\n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/axes_grid/bivariate_normal.npy  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/eeg.dat  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/logo2.png  \n",
            "  inflating: 3rd_lib/matplotlib/mpl-data/sample_data/msft.csv  \n",
            "   creating: 3rd_lib/matplotlib/__pycache__/\n",
            "  inflating: 3rd_lib/matplotlib/__pycache__/figure.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/matplotlib/__pycache__/typing.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/matplotlib/__pycache__/widgets.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/matplotlib/__pycache__/_cm.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/matplotlib/__pycache__/patches.cpython-310.pyc  \n",
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            "  inflating: 3rd_lib/matplotlib/__pycache__/rcsetup.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/matplotlib/__pycache__/mlab.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/matplotlib/__pycache__/_type1font.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/matplotlib/__pycache__/_pylab_helpers.cpython-310.pyc  \n",
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            "  inflating: 3rd_lib/matplotlib/__pycache__/backend_bases.cpython-310.pyc  \n",
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            "  inflating: 3rd_lib/matplotlib/tests/test_preprocess_data.py  \n",
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            "  inflating: 3rd_lib/matplotlib/tests/test_compare_images.py  \n",
            "  inflating: 3rd_lib/matplotlib/tests/test_backend_pdf.py  \n",
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            "  inflating: 3rd_lib/matplotlib/tests/test_ticker.py  \n",
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            "  inflating: 3rd_lib/pyparsing-3.1.2.dist-info/METADATA  \n",
            " extracting: 3rd_lib/pyparsing-3.1.2.dist-info/WHEEL  \n",
            "  inflating: 3rd_lib/pyparsing-3.1.2.dist-info/LICENSE  \n",
            " extracting: 3rd_lib/pyparsing-3.1.2.dist-info/INSTALLER  \n",
            "  inflating: 3rd_lib/pyparsing-3.1.2.dist-info/RECORD  \n",
            "   creating: 3rd_lib/bin/\n",
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            "  inflating: 3rd_lib/executing-2.0.1.dist-info/WHEEL  \n",
            " extracting: 3rd_lib/executing-2.0.1.dist-info/top_level.txt  \n",
            " extracting: 3rd_lib/executing-2.0.1.dist-info/INSTALLER  \n",
            "  inflating: 3rd_lib/executing-2.0.1.dist-info/RECORD  \n",
            "   creating: 3rd_lib/matplotlib-3.9.0.dist-info/\n",
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            " extracting: 3rd_lib/matplotlib-3.9.0.dist-info/INSTALLER  \n",
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            "  inflating: 3rd_lib/matplotlib-3.9.0.dist-info/METADATA  \n",
            "   creating: 3rd_lib/dash_core_components-2.0.0.dist-info/\n",
            " extracting: 3rd_lib/dash_core_components-2.0.0.dist-info/INSTALLER  \n",
            "  inflating: 3rd_lib/dash_core_components-2.0.0.dist-info/RECORD  \n",
            "  inflating: 3rd_lib/dash_core_components-2.0.0.dist-info/METADATA  \n",
            " extracting: 3rd_lib/dash_core_components-2.0.0.dist-info/WHEEL  \n",
            "  inflating: 3rd_lib/dash_core_components-2.0.0.dist-info/LICENSE  \n",
            " extracting: 3rd_lib/dash_core_components-2.0.0.dist-info/top_level.txt  \n",
            "   creating: 3rd_lib/urllib3-2.2.1.dist-info/\n",
            "  inflating: 3rd_lib/urllib3-2.2.1.dist-info/WHEEL  \n",
            "   creating: 3rd_lib/urllib3-2.2.1.dist-info/licenses/\n",
            "  inflating: 3rd_lib/urllib3-2.2.1.dist-info/licenses/LICENSE.txt  \n",
            " extracting: 3rd_lib/urllib3-2.2.1.dist-info/INSTALLER  \n",
            "  inflating: 3rd_lib/urllib3-2.2.1.dist-info/RECORD  \n",
            "  inflating: 3rd_lib/urllib3-2.2.1.dist-info/METADATA  \n",
            "   creating: 3rd_lib/contourpy-1.2.1.dist-info/\n",
            "  inflating: 3rd_lib/contourpy-1.2.1.dist-info/WHEEL  \n",
            "  inflating: 3rd_lib/contourpy-1.2.1.dist-info/LICENSE  \n",
            " extracting: 3rd_lib/contourpy-1.2.1.dist-info/INSTALLER  \n",
            "  inflating: 3rd_lib/contourpy-1.2.1.dist-info/RECORD  \n",
            "  inflating: 3rd_lib/contourpy-1.2.1.dist-info/METADATA  \n",
            "   creating: 3rd_lib/parso-0.8.4.dist-info/\n",
            "  inflating: 3rd_lib/parso-0.8.4.dist-info/AUTHORS.txt  \n",
            "  inflating: 3rd_lib/parso-0.8.4.dist-info/RECORD  \n",
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            "  inflating: 3rd_lib/parso-0.8.4.dist-info/METADATA  \n",
            "  inflating: 3rd_lib/parso-0.8.4.dist-info/WHEEL  \n",
            " extracting: 3rd_lib/parso-0.8.4.dist-info/top_level.txt  \n",
            " extracting: 3rd_lib/parso-0.8.4.dist-info/INSTALLER  \n",
            "   creating: 3rd_lib/python_dateutil-2.9.0.post0.dist-info/\n",
            "  inflating: 3rd_lib/python_dateutil-2.9.0.post0.dist-info/LICENSE  \n",
            " extracting: 3rd_lib/python_dateutil-2.9.0.post0.dist-info/top_level.txt  \n",
            " extracting: 3rd_lib/python_dateutil-2.9.0.post0.dist-info/INSTALLER  \n",
            "  inflating: 3rd_lib/python_dateutil-2.9.0.post0.dist-info/RECORD  \n",
            " extracting: 3rd_lib/python_dateutil-2.9.0.post0.dist-info/zip-safe  \n",
            "  inflating: 3rd_lib/python_dateutil-2.9.0.post0.dist-info/METADATA  \n",
            "  inflating: 3rd_lib/python_dateutil-2.9.0.post0.dist-info/WHEEL  \n",
            "   creating: 3rd_lib/parso/\n",
            "  inflating: 3rd_lib/parso/grammar.py  \n",
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            "  inflating: 3rd_lib/parso/python/tokenize.py  \n",
            "  inflating: 3rd_lib/parso/python/grammar310.txt  \n",
            "  inflating: 3rd_lib/parso/python/errors.py  \n",
            "  inflating: 3rd_lib/parso/python/tree.py  \n",
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            "  inflating: 3rd_lib/parso/python/pep8.py  \n",
            "   creating: 3rd_lib/parso/python/__pycache__/\n",
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            "  inflating: 3rd_lib/parso/python/__pycache__/diff.cpython-310.pyc  \n",
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            "  inflating: 3rd_lib/parso/python/__pycache__/pep8.cpython-310.pyc  \n",
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            "  inflating: 3rd_lib/parso/python/grammar38.txt  \n",
            "  inflating: 3rd_lib/parso/python/parser.py  \n",
            " extracting: 3rd_lib/parso/python/__init__.py  \n",
            "  inflating: 3rd_lib/parso/file_io.py  \n",
            "  inflating: 3rd_lib/parso/parser.py  \n",
            " extracting: 3rd_lib/parso/py.typed  \n",
            "   creating: 3rd_lib/parso/pgen2/\n",
            "  inflating: 3rd_lib/parso/pgen2/__init__.py  \n",
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            "   creating: 3rd_lib/parso/pgen2/__pycache__/\n",
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            "  inflating: 3rd_lib/parso/pgen2/__pycache__/generator.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/parso/normalizer.py  \n",
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            "  inflating: 3rd_lib/parso/__pycache__/parser.cpython-310.pyc  \n",
            "   creating: 3rd_lib/referencing-0.35.1.dist-info/\n",
            "   creating: 3rd_lib/referencing-0.35.1.dist-info/licenses/\n",
            "  inflating: 3rd_lib/referencing-0.35.1.dist-info/licenses/COPYING  \n",
            " extracting: 3rd_lib/referencing-0.35.1.dist-info/INSTALLER  \n",
            "  inflating: 3rd_lib/referencing-0.35.1.dist-info/RECORD  \n",
            "  inflating: 3rd_lib/referencing-0.35.1.dist-info/METADATA  \n",
            "  inflating: 3rd_lib/referencing-0.35.1.dist-info/WHEEL  \n",
            "   creating: 3rd_lib/jsonschema-4.22.0.dist-info/\n",
            "  inflating: 3rd_lib/jsonschema-4.22.0.dist-info/entry_points.txt  \n",
            "  inflating: 3rd_lib/jsonschema-4.22.0.dist-info/METADATA  \n",
            "  inflating: 3rd_lib/jsonschema-4.22.0.dist-info/WHEEL  \n",
            "   creating: 3rd_lib/jsonschema-4.22.0.dist-info/licenses/\n",
            "  inflating: 3rd_lib/jsonschema-4.22.0.dist-info/licenses/COPYING  \n",
            " extracting: 3rd_lib/jsonschema-4.22.0.dist-info/INSTALLER  \n",
            "  inflating: 3rd_lib/jsonschema-4.22.0.dist-info/RECORD  \n",
            "   creating: 3rd_lib/prompt_toolkit-3.0.45.dist-info/\n",
            " extracting: 3rd_lib/prompt_toolkit-3.0.45.dist-info/WHEEL  \n",
            " extracting: 3rd_lib/prompt_toolkit-3.0.45.dist-info/INSTALLER  \n",
            "  inflating: 3rd_lib/prompt_toolkit-3.0.45.dist-info/RECORD  \n",
            "  inflating: 3rd_lib/prompt_toolkit-3.0.45.dist-info/AUTHORS.rst  \n",
            "  inflating: 3rd_lib/prompt_toolkit-3.0.45.dist-info/METADATA  \n",
            " extracting: 3rd_lib/prompt_toolkit-3.0.45.dist-info/top_level.txt  \n",
            "  inflating: 3rd_lib/prompt_toolkit-3.0.45.dist-info/LICENSE  \n",
            "   creating: 3rd_lib/stack_data/\n",
            " extracting: 3rd_lib/stack_data/version.py  \n",
            "   creating: 3rd_lib/stack_data/__pycache__/\n",
            "  inflating: 3rd_lib/stack_data/__pycache__/core.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/stack_data/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/stack_data/__pycache__/serializing.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/stack_data/__pycache__/utils.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/stack_data/__pycache__/formatting.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/stack_data/__pycache__/version.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/stack_data/utils.py  \n",
            "  inflating: 3rd_lib/stack_data/__init__.py  \n",
            "  inflating: 3rd_lib/stack_data/serializing.py  \n",
            " extracting: 3rd_lib/stack_data/py.typed  \n",
            "  inflating: 3rd_lib/stack_data/core.py  \n",
            "  inflating: 3rd_lib/stack_data/formatting.py  \n",
            "   creating: 3rd_lib/retrying-1.3.4.dist-info/\n",
            " extracting: 3rd_lib/retrying-1.3.4.dist-info/INSTALLER  \n",
            "  inflating: 3rd_lib/retrying-1.3.4.dist-info/RECORD  \n",
            "  inflating: 3rd_lib/retrying-1.3.4.dist-info/AUTHORS.rst  \n",
            "  inflating: 3rd_lib/retrying-1.3.4.dist-info/NOTICE  \n",
            "  inflating: 3rd_lib/retrying-1.3.4.dist-info/METADATA  \n",
            " extracting: 3rd_lib/retrying-1.3.4.dist-info/WHEEL  \n",
            "  inflating: 3rd_lib/retrying-1.3.4.dist-info/LICENSE  \n",
            " extracting: 3rd_lib/retrying-1.3.4.dist-info/top_level.txt  \n",
            "   creating: 3rd_lib/ConfigArgParse-1.7.dist-info/\n",
            " extracting: 3rd_lib/ConfigArgParse-1.7.dist-info/WHEEL  \n",
            "  inflating: 3rd_lib/ConfigArgParse-1.7.dist-info/LICENSE  \n",
            " extracting: 3rd_lib/ConfigArgParse-1.7.dist-info/top_level.txt  \n",
            " extracting: 3rd_lib/ConfigArgParse-1.7.dist-info/INSTALLER  \n",
            "  inflating: 3rd_lib/ConfigArgParse-1.7.dist-info/RECORD  \n",
            "  inflating: 3rd_lib/ConfigArgParse-1.7.dist-info/METADATA  \n",
            "   creating: 3rd_lib/meshio/\n",
            "   creating: 3rd_lib/meshio/su2/\n",
            "   creating: 3rd_lib/meshio/su2/__pycache__/\n",
            "  inflating: 3rd_lib/meshio/su2/__pycache__/_su2.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/meshio/su2/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/meshio/su2/__init__.py  \n",
            "  inflating: 3rd_lib/meshio/su2/_su2.py  \n",
            "  inflating: 3rd_lib/meshio/__init__.py  \n",
            "   creating: 3rd_lib/meshio/medit/\n",
            "   creating: 3rd_lib/meshio/medit/__pycache__/\n",
            "  inflating: 3rd_lib/meshio/medit/__pycache__/_medit_internal.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/meshio/medit/__pycache__/_medit.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/meshio/medit/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: 3rd_lib/meshio/medit/__init__.py  \n",
            "  inflating: 3rd_lib/meshio/medit/_medit.py  \n",
            "  inflating: 3rd_lib/meshio/medit/_medit_internal.py  \n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import paddle\n",
        "import torch\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "print(\"PaddlePaddle Version:\", paddle.__version__)\n",
        "print(\"PyTorch Version:\", torch.__version__)\n",
        "print(\"TensorFlow Version:\", tf.__version__)\n",
        "print(\"NumPy Version:\", np.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0zeE_pIqevs",
        "outputId": "75d95cf8-8895-4184-94e0-d501cc6d1e13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PaddlePaddle Version: 2.6.1\n",
            "PyTorch Version: 2.3.1+cu121\n",
            "TensorFlow Version: 2.15.0\n",
            "NumPy Version: 1.25.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import tensorflow as tf\n",
        "import random\n",
        "import os\n",
        "\n",
        "def seed_everything(seed=42):\n",
        "    random.seed(seed)           # Python 内置的随机库\n",
        "    np.random.seed(seed)        # NumPy 随机数种子\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)  # 环境变量\n",
        "\n",
        "    # TensorFlow 设置\n",
        "    tf.random.set_seed(seed)\n",
        "    # 对于单 GPU 或 CPU，以下设置不是必须的\n",
        "    os.environ['TF_DETERMINISTIC_OPS'] = '1'\n",
        "    os.environ['TF_CUDNN_DETERMINISTIC'] = '1'\n",
        "\n",
        "    # PyTorch 设置\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)  # 如果使用多 GPU\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "\n",
        "    # PaddlePaddle 设置\n",
        "    paddle.seed(seed)\n",
        "\n",
        "# 调用函数以设置种子\n",
        "seed_everything(42)"
      ],
      "metadata": {
        "id": "0HLYM5Fiqlqi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p data/data_train_B\n",
        "!unzip -o train_track_B.zip -d data/data_train_B/\n",
        "!unzip -o mesh_B_0603.zip -d data/data_train_B/\n",
        "!mv data/data_train_B/train_mesh_0603/*.ply data/data_train_B/\n",
        "!unzip -o track_B.zip -d data/\n",
        "!mv data/track_B data/data_test_B/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KKSwihDPqn8G",
        "outputId": "9cd68891-7d9e-455b-d514-0ac47735b9bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  train_track_B.zip\n",
            "  inflating: data/data_train_B/area_0002.npy  \n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install faiss-cpu"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sz-ym923qqDX",
        "outputId": "2705e65a-0e44-436e-83e1-a017d916c78c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting faiss-cpu\n",
            "  Downloading faiss_cpu-1.8.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from faiss-cpu) (1.25.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from faiss-cpu) (24.1)\n",
            "Installing collected packages: faiss-cpu\n",
            "Successfully installed faiss-cpu-1.8.0.post1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import faiss\n",
        "import os\n",
        "from tqdm import tqdm, trange\n",
        "from concurrent.futures import ThreadPoolExecutor, as_completed\n",
        "\n",
        "class OptimizedFAISSKNNPressurePredictor:\n",
        "    def __init__(self, k=3, batch_size=1000, num_workers=4):\n",
        "        self.k = k\n",
        "        self.batch_size = batch_size\n",
        "        self.num_workers = num_workers\n",
        "        self.index = None\n",
        "        self.pressures = None\n",
        "\n",
        "    def preprocess_data(self, data_dir):\n",
        "        mesh_files = [f for f in os.listdir(data_dir) if f.startswith('mesh_') and f.endswith('.ply')]\n",
        "        mesh_indices = sorted([int(f.split('_')[1].split('.')[0]) for f in mesh_files])\n",
        "\n",
        "        features = []\n",
        "        pressures = []\n",
        "\n",
        "        def process_file(idx):\n",
        "            centroid = np.load(os.path.join(data_dir, f\"centroid_{str(idx).zfill(4)}.npy\"))\n",
        "            area = np.load(os.path.join(data_dir, f\"area_{str(idx).zfill(4)}.npy\"))\n",
        "            pressure = np.load(os.path.join(data_dir, f\"press_{str(idx).zfill(4)}.npy\"))\n",
        "            return np.concatenate([centroid, area[:, np.newaxis]], axis=1), pressure\n",
        "\n",
        "        with ThreadPoolExecutor(max_workers=self.num_workers) as executor:\n",
        "            futures = [executor.submit(process_file, idx) for idx in mesh_indices]\n",
        "            for future in tqdm(as_completed(futures), total=len(mesh_indices), desc=\"Preprocessing training data\"):\n",
        "                feature, pressure = future.result()\n",
        "                features.append(feature)\n",
        "                pressures.append(pressure)\n",
        "\n",
        "        self.features = np.concatenate(features, axis=0).astype(np.float32)\n",
        "        self.pressures = np.concatenate(pressures, axis=0).astype(np.float32)\n",
        "\n",
        "    def build_index(self):\n",
        "        d = self.features.shape[1]\n",
        "        nlist = 100  # 聚类中心的数量，可以根据数据集大小调整\n",
        "\n",
        "        # 使用CPU版本的IndexIVFFlat\n",
        "        quantizer = faiss.IndexFlatL2(d)\n",
        "        self.index = faiss.IndexIVFFlat(quantizer, d, nlist, faiss.METRIC_L2)\n",
        "\n",
        "        print(\"Training FAISS index...\")\n",
        "        self.index.train(self.features)\n",
        "\n",
        "        print(\"Adding vectors to FAISS index...\")\n",
        "        for i in trange(0, len(self.features), 100000, desc=\"Adding to index\"):\n",
        "            self.index.add(self.features[i:i+self.batch_size])\n",
        "\n",
        "    def predict_batch(self, features):\n",
        "        _, I = self.index.search(features, self.k)\n",
        "        nearest_pressures = self.pressures[I]\n",
        "        return np.mean(nearest_pressures, axis=1)\n",
        "\n",
        "    def predict(self, test_data_dir):\n",
        "        mesh_files = [f for f in os.listdir(test_data_dir) if f.startswith('mesh_') and f.endswith('.ply')]\n",
        "        mesh_indices = sorted([int(f.split('_')[1].split('.')[0]) for f in mesh_files])\n",
        "\n",
        "        def load_test_features(idx):\n",
        "            centroid = np.load(os.path.join(test_data_dir, f\"centroid_{str(idx)}.npy\"))\n",
        "            area = np.load(os.path.join(test_data_dir, f\"area_{str(idx)}.npy\"))\n",
        "            return np.concatenate([centroid, area[:, np.newaxis]], axis=1)\n",
        "\n",
        "        predictions = []\n",
        "        os.makedirs(\"./output/optimized_faiss_knn_predictions\", exist_ok=True)\n",
        "\n",
        "        with ThreadPoolExecutor(max_workers=self.num_workers) as executor:\n",
        "            feature_futures = {executor.submit(load_test_features, idx): idx for idx in mesh_indices}\n",
        "\n",
        "            for future in tqdm(as_completed(feature_futures), total=len(mesh_indices), desc=\"Processing test data\"):\n",
        "                idx = feature_futures[future]\n",
        "                features = future.result()\n",
        "\n",
        "                batch_predictions = []\n",
        "                for i in range(0, len(features), self.batch_size):\n",
        "                    batch_features = features[i:i+self.batch_size]\n",
        "                    batch_predictions.extend(self.predict_batch(batch_features))\n",
        "\n",
        "                output_path = os.path.join(\"./output/optimized_faiss_knn_predictions\", f\"press_{str(idx).zfill(4)}.npy\")\n",
        "                np.save(output_path, np.array(batch_predictions))\n",
        "                predictions.extend(batch_predictions)\n",
        "\n",
        "        return predictions\n",
        "\n",
        "predictor = OptimizedFAISSKNNPressurePredictor(k=1, batch_size=1000, num_workers=1)\n",
        "\n",
        "print(\"Preprocessing training data...\")\n",
        "predictor.preprocess_data(\"./data/data_train_B\")\n",
        "\n",
        "print(\"Building FAISS index...\")\n",
        "predictor.build_index()\n",
        "\n",
        "print(\"Predicting on test data...\")\n",
        "predictions = predictor.predict(\"./data/data_test_B\")\n",
        "\n",
        "print(\"Prediction completed.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "naqkJfbTqtmT",
        "outputId": "ffefc37f-55f1-4fd5-9f71-1dca41648146"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Preprocessing training data...\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Preprocessing training data: 100%|██████████| 500/500 [00:08<00:00, 59.24it/s]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Building FAISS index...\n",
            "Training FAISS index...\n",
            "Adding vectors to FAISS index...\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Adding to index: 100%|██████████| 1859/1859 [00:00<00:00, 7655.97it/s]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicting on test data...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Processing test data: 100%|██████████| 50/50 [41:49<00:00, 50.18s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction completed.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r ./output/optimized_faiss_knn_predictions/* ./result/"
      ],
      "metadata": {
        "id": "Nflk68LVqxq0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r B_result.zip ./result/*"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QiDIux6zo3Qj",
        "outputId": "459744db-ad71-4012-9b3c-f4ec53f6b4c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: result/press_0001.npy (deflated 17%)\n",
            "  adding: result/press_0002.npy (deflated 22%)\n",
            "  adding: result/press_0003.npy (deflated 16%)\n",
            "  adding: result/press_0004.npy (deflated 16%)\n",
            "  adding: result/press_0005.npy (deflated 18%)\n",
            "  adding: result/press_0006.npy (deflated 18%)\n",
            "  adding: result/press_0007.npy (deflated 17%)\n",
            "  adding: result/press_0008.npy (deflated 18%)\n",
            "  adding: result/press_0009.npy (deflated 17%)\n",
            "  adding: result/press_0010.npy (deflated 16%)\n",
            "  adding: result/press_0011.npy (deflated 17%)\n",
            "  adding: result/press_0012.npy (deflated 18%)\n",
            "  adding: result/press_0013.npy (deflated 18%)\n",
            "  adding: result/press_0014.npy (deflated 18%)\n",
            "  adding: result/press_0015.npy (deflated 17%)\n",
            "  adding: result/press_0016.npy (deflated 21%)\n",
            "  adding: result/press_0017.npy (deflated 18%)\n",
            "  adding: result/press_0018.npy (deflated 18%)\n",
            "  adding: result/press_0019.npy (deflated 16%)\n",
            "  adding: result/press_0020.npy (deflated 18%)\n",
            "  adding: result/press_0021.npy (deflated 18%)\n",
            "  adding: result/press_0022.npy (deflated 18%)\n",
            "  adding: result/press_0023.npy (deflated 17%)\n",
            "  adding: result/press_0024.npy (deflated 18%)\n",
            "  adding: result/press_0025.npy (deflated 16%)\n",
            "  adding: result/press_0026.npy (deflated 18%)\n",
            "  adding: result/press_0027.npy (deflated 17%)\n",
            "  adding: result/press_0028.npy (deflated 17%)\n",
            "  adding: result/press_0029.npy (deflated 19%)\n",
            "  adding: result/press_0030.npy (deflated 18%)\n",
            "  adding: result/press_0031.npy (deflated 18%)\n",
            "  adding: result/press_0032.npy (deflated 17%)\n",
            "  adding: result/press_0033.npy (deflated 16%)\n",
            "  adding: result/press_0034.npy (deflated 16%)\n",
            "  adding: result/press_0035.npy (deflated 17%)\n",
            "  adding: result/press_0036.npy (deflated 18%)\n",
            "  adding: result/press_0037.npy (deflated 16%)\n",
            "  adding: result/press_0038.npy (deflated 17%)\n",
            "  adding: result/press_0039.npy (deflated 16%)\n",
            "  adding: result/press_0040.npy (deflated 18%)\n",
            "  adding: result/press_0041.npy (deflated 17%)\n",
            "  adding: result/press_0042.npy (deflated 17%)\n",
            "  adding: result/press_0043.npy (deflated 18%)\n",
            "  adding: result/press_0044.npy (deflated 18%)\n",
            "  adding: result/press_0045.npy (deflated 17%)\n",
            "  adding: result/press_0046.npy (deflated 21%)\n",
            "  adding: result/press_0047.npy (deflated 17%)\n",
            "  adding: result/press_0048.npy (deflated 17%)\n",
            "  adding: result/press_0049.npy (deflated 18%)\n",
            "  adding: result/press_0050.npy (deflated 20%)\n",
            "  adding: result/press_658.npy (deflated 48%)\n",
            "  adding: result/press_659.npy (deflated 48%)\n",
            "  adding: result/press_660.npy (deflated 48%)\n",
            "  adding: result/press_662.npy (deflated 48%)\n",
            "  adding: result/press_663.npy (deflated 48%)\n",
            "  adding: result/press_664.npy (deflated 48%)\n",
            "  adding: result/press_665.npy (deflated 48%)\n",
            "  adding: result/press_666.npy (deflated 48%)\n",
            "  adding: result/press_667.npy (deflated 48%)\n",
            "  adding: result/press_668.npy (deflated 48%)\n",
            "  adding: result/press_672.npy (deflated 48%)\n",
            "  adding: result/press_673.npy (deflated 48%)\n",
            "  adding: result/press_674.npy (deflated 48%)\n",
            "  adding: result/press_675.npy (deflated 48%)\n",
            "  adding: result/press_676.npy (deflated 48%)\n",
            "  adding: result/press_677.npy (deflated 48%)\n",
            "  adding: result/press_678.npy (deflated 48%)\n",
            "  adding: result/press_679.npy (deflated 48%)\n",
            "  adding: result/press_681.npy (deflated 48%)\n",
            "  adding: result/press_683.npy (deflated 48%)\n",
            "  adding: result/press_684.npy (deflated 48%)\n",
            "  adding: result/press_686.npy (deflated 48%)\n",
            "  adding: result/press_687.npy (deflated 48%)\n",
            "  adding: result/press_688.npy (deflated 48%)\n",
            "  adding: result/press_689.npy (deflated 48%)\n",
            "  adding: result/press_690.npy (deflated 47%)\n",
            "  adding: result/press_691.npy (deflated 48%)\n",
            "  adding: result/press_692.npy (deflated 48%)\n",
            "  adding: result/press_693.npy (deflated 47%)\n",
            "  adding: result/press_695.npy (deflated 48%)\n",
            "  adding: result/press_696.npy (deflated 48%)\n",
            "  adding: result/press_697.npy (deflated 48%)\n",
            "  adding: result/press_700.npy (deflated 48%)\n",
            "  adding: result/press_701.npy (deflated 48%)\n",
            "  adding: result/press_702.npy (deflated 48%)\n",
            "  adding: result/press_703.npy (deflated 48%)\n",
            "  adding: result/press_704.npy (deflated 48%)\n",
            "  adding: result/press_705.npy (deflated 48%)\n",
            "  adding: result/press_708.npy (deflated 48%)\n",
            "  adding: result/press_709.npy (deflated 47%)\n",
            "  adding: result/press_710.npy (deflated 48%)\n",
            "  adding: result/press_711.npy (deflated 48%)\n",
            "  adding: result/press_712.npy (deflated 48%)\n",
            "  adding: result/press_713.npy (deflated 48%)\n",
            "  adding: result/press_715.npy (deflated 48%)\n",
            "  adding: result/press_717.npy (deflated 48%)\n",
            "  adding: result/press_718.npy (deflated 48%)\n",
            "  adding: result/press_719.npy (deflated 48%)\n",
            "  adding: result/press_721.npy (deflated 48%)\n",
            "  adding: result/press_722.npy (deflated 48%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XRIiA6m2q5LQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}